From f69675be6ed2950266a5b6d1d47e7f0ae1c54512 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Joanna=20Pylv=C3=A4n=C3=A4inen?= Date: Tue, 24 Jun 2025 22:37:44 +0300 Subject: [PATCH] some bugs fixed at least the prediction part works for in this notebook - training part has not been tested. Please merge if feels appropriate --- .../Cellpose_2D_ZeroCostDL4Mic.ipynb | 2582 +---------------- 1 file changed, 1 insertion(+), 2581 deletions(-) diff --git a/Colab_notebooks/Cellpose_2D_ZeroCostDL4Mic.ipynb b/Colab_notebooks/Cellpose_2D_ZeroCostDL4Mic.ipynb index 831929c9..d1e04671 100644 --- a/Colab_notebooks/Cellpose_2D_ZeroCostDL4Mic.ipynb +++ b/Colab_notebooks/Cellpose_2D_ZeroCostDL4Mic.ipynb @@ -1,2581 +1 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "V9zNGvape2-I" - }, - "source": [ - "# **Cellpose - Omnipose (2D)**\n", - "\n", - "---\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "pwLsIXtEw3Kx" - }, - "source": [ - "**Cellpose 2D** is a deep-learning method that can be used to segment cell and/or nuclei from bioimages and was first published by [Stringer *et al.* in 2020, in Nature Method](https://www.nature.com/articles/s41592-020-01018-x). Omnipose was developped by [Kevin John Cutler and colleagues](https://www.biorxiv.org/content/10.1101/2021.11.03.467199v2).\n", - "\n", - " **This particular notebook enables segmentation of 2D and 3D datasets. Hovewer, training is limited to 2D images. If you are interested in using 3D datasets for training, you should use the StarDist or U-Net 3D notebooks instead.**\n", - "\n", - "---\n", - "*Disclaimer*:\n", - "\n", - "This notebook is part of the Zero-Cost Deep-Learning to Enhance Microscopy project (https://github.com/HenriquesLab/DeepLearning_Collab/wiki). Jointly developed by the Jacquemet (link to https://cellmig.org/) and Henriques (https://henriqueslab.github.io/) laboratories.\n", - "\n", - "This notebook is largely based on the paper:\n", - "\n", - "**Cellpose: a generalist algorithm for cellular segmentation** from Stringer *et al.*, Nature Methods, 2020. (https://www.nature.com/articles/s41592-020-01018-x)\n", - "\n", - "**Omnipose: a high-precision morphology-independent solution for bacterial cell segmentation** from Kevin J. Cutler et al, biorXiv, 2021. (https://www.biorxiv.org/content/10.1101/2021.11.03.467199v2)\n", - "\n", - "**The Original code** is freely available in GitHub:\n", - "https://github.com/MouseLand/cellpose\n", - "\n", - "**Please also cite these original papers when using or developing this notebook.**\n", - "\n", - "**This notebook was also inspired by the one created by @pr4deepr** which is available here:\n", - "https://colab.research.google.com/github/MouseLand/cellpose/blob/8dadbe528b1ee3f59533bc23ab0ed371d5e7b769/notebooks/Cellpose_2D_v0_1.ipynb\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "C5oYf0Q5yXrl" - }, - "source": [ - "# **0. Before getting started**\n", - "---\n", - " For Cellpose to train, **it needs to have access to a paired training dataset made of images and their corresponding masks (label images)**. Information on how to generate a training dataset is available in our Wiki page: https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki\n", - "\n", - "**We strongly recommend that you generate extra paired images. These images can be used to assess the quality of your trained model**. The quality control assessment can be done directly in this notebook.\n", - "\n", - "The data structure is important. It is necessary that all the input data are in the same folder and that all the output data is in a separate folder. The provided training dataset is already split in two folders called \"Training - Images\" (Training_source) and \"Training - Masks\" (Training_target).\n", - "\n", - "Additionally, the corresponding Training_source and Training_target files need to have **the same name**.\n", - "\n", - " **Use 8/16 bit png or Tiff images**.\n", - "\n", - "\n", - "You can also provide a folder that contains the data that you wish to analyse with the trained network once all training has been performed. This can include Test dataset for which you have the equivalent output and can compare to what the network provides.\n", - "\n", - "Here's a common data structure that can work:\n", - "* Experiment A\n", - " - **Training dataset**\n", - " - Images (Training_source)\n", - " - img_1.tif, img_2.tif, ...\n", - " - Label images (Training_target)\n", - " - img_1.tif, img_2.tif, ...\n", - " - **Quality control dataset**\n", - " - Images\n", - " - img_1.tif, img_2.tif\n", - " - Masks\n", - " - img_1.tif, img_2.tif\n", - " - **Data to be predicted**\n", - " - **Results**\n", - "\n", - "---\n", - "**Important note**\n", - "\n", - "- If you wish to **Train a network from scratch** using your own dataset (and we encourage everyone to do that), you will need to run **sections 1 - 4**, then use **section 5** to assess the quality of your model and **section 6** to run predictions using the model that you trained.\n", - "\n", - "- If you wish to **Evaluate your model** using a model previously generated and saved on your Google Drive, you will only need to run **sections 1 and 2** to set up the notebook, then use **section 5** to assess the quality of your model.\n", - "\n", - "- If you only wish to **run predictions** using a pretrained model you will only need to run **sections 1 and 2** to set up the notebook, then use **section 6** to run the predictions on the desired model.\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "n4yWFoJNnoin" - }, - "source": [ - "# **1. Install Cellpose and dependencies**\n", - "---\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WlPYHrPHlUtl" - }, - "source": [ - "\n", - "## **1.1. Install dependencies**\n", - "---\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "id": "3u2mXn3XsWzd" - }, - "outputs": [], - "source": [ - "\n", - "#@markdown ##Install Cellpose\n", - "\n", - "#Libraries contains information of certain topics.\n", - "#For example the tifffile library contains information on how to handle tif-files.\n", - "\n", - "#Here, we install libraries which are not already included in Colab.\n", - "\n", - "!pip uninstall -y -q yellowbrick\n", - "\n", - "!pip install -q tifffile # contains tools to operate tiff-files\n", - "!pip install -q folium==0.2.1\n", - "!pip install -q imgaug==0.2.5\n", - "!pip install \"opencv-python-headless<4.3\"\n", - "#!pip install -q numpy==1.20.0\n", - "!pip install -q cellpose\n", - "!pip install -q wget\n", - "!pip install -q memory_profiler\n", - "!pip install -q fpdf2\n", - "\n", - "\n", - "\n", - "#Force session restart\n", - "exit(0)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "w-FE-qZI5U_k" - }, - "source": [ - "\n", - "## **1.2. Restart your runtime**\n", - "---\n", - "\n", - "\n", - "\n", - "** Ignore the following message error message. Your Runtime has automatically restarted. This is normal.**\n", - "\n", - "\"\"
\n", - "\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "wgeU4cC55Sej" - }, - "source": [ - "## **1.3. Load key dependencies**\n", - "---\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "id": "Kkbr0GAA5ZAQ" - }, - "outputs": [], - "source": [ - "#@markdown ##Load key dependencies\n", - "\n", - "Notebook_version = '1.16.3'\n", - "Network = 'Cellpose'\n", - "\n", - "from builtins import any as b_any\n", - "\n", - "%load_ext memory_profiler\n", - "\n", - "def get_requirements_path():\n", - " # Store requirements file in 'contents' directory\n", - " current_dir = os.getcwd()\n", - " dir_count = current_dir.count('/') - 1\n", - " path = '../' * (dir_count) + 'requirements.txt'\n", - " return path\n", - "\n", - "def filter_files(file_list, filter_list):\n", - " filtered_list = []\n", - " for fname in file_list:\n", - " if b_any(fname.split('==')[0] in s for s in filter_list):\n", - " filtered_list.append(fname)\n", - " return filtered_list\n", - "\n", - "def build_requirements_file(before, after):\n", - " path = get_requirements_path()\n", - "\n", - " # Exporting requirements.txt for local run\n", - " !pip freeze > $path\n", - "\n", - " # Get minimum requirements file\n", - " df = pd.read_csv(path)\n", - " mod_list = [m.split('.')[0] for m in after if not m in before]\n", - " req_list_temp = df.values.tolist()\n", - " req_list = [x[0] for x in req_list_temp]\n", - "\n", - " # Replace with package name and handle cases where import name is different to module name\n", - " mod_name_list = [['sklearn', 'scikit-learn'], ['skimage', 'scikit-image']]\n", - " mod_replace_list = [[x[1] for x in mod_name_list] if s in [x[0] for x in mod_name_list] else s for s in mod_list]\n", - " filtered_list = filter_files(req_list, mod_replace_list)\n", - "\n", - " file=open(path,'w')\n", - " for item in filtered_list:\n", - " file.writelines(item + '\\n')\n", - "\n", - " file.close()\n", - "\n", - "import sys\n", - "before = [str(m) for m in sys.modules]\n", - "\n", - "\n", - "\n", - "# ------- Variable specific to Cellpose -------\n", - "\n", - "from urllib.parse import urlparse\n", - "%matplotlib inline\n", - "from cellpose import models\n", - "#use_GPU = models.use_gpu()\n", - "\n", - "#import mxnet as mx\n", - "\n", - "from skimage.util import img_as_ubyte\n", - "import cv2\n", - "from cellpose import plot\n", - "from ipywidgets import interact, interact_manual\n", - "from zipfile import ZIP_DEFLATED\n", - "\n", - "\n", - "# For sliders and dropdown menu and progress bar\n", - "from ipywidgets import interact\n", - "import ipywidgets as widgets\n", - "\n", - "\n", - "# ------- Common variable to all ZeroCostDL4Mic notebooks -------\n", - "import numpy as np\n", - "from matplotlib import pyplot as plt\n", - "import urllib\n", - "import os, random\n", - "import shutil\n", - "import zipfile\n", - "from tifffile import imread, imsave\n", - "import time\n", - "import sys\n", - "import wget\n", - "from pathlib import Path\n", - "import pandas as pd\n", - "import csv\n", - "from glob import glob\n", - "from scipy import signal\n", - "from scipy import ndimage\n", - "from skimage import io\n", - "from sklearn.linear_model import LinearRegression\n", - "from skimage.util import img_as_uint\n", - "import matplotlib as mpl\n", - "from skimage.metrics import structural_similarity\n", - "from skimage.metrics import peak_signal_noise_ratio as psnr\n", - "from astropy.visualization import simple_norm\n", - "from skimage import img_as_float32\n", - "from skimage.util import img_as_ubyte\n", - "from tqdm import tqdm\n", - "from fpdf import FPDF, HTMLMixin\n", - "from datetime import datetime\n", - "import subprocess\n", - "from pip._internal.operations.freeze import freeze\n", - "\n", - "\n", - "#Create a variable to get and store relative base path\n", - "base_path = os.getcwd()\n", - "\n", - "# Colors for the warning messages\n", - "class bcolors:\n", - " WARNING = '\\033[31m'\n", - "\n", - "W = '\\033[0m' # white (normal)\n", - "R = '\\033[31m' # red\n", - "\n", - "#Disable some of the tensorflow warnings\n", - "import warnings\n", - "warnings.filterwarnings(\"ignore\")\n", - "\n", - "print(\"Libraries installed\")\n", - "\n", - "# Check if this is the latest version of the notebook\n", - "All_notebook_versions = pd.read_csv(\"https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/Latest_Notebook_versions.csv\", dtype=str)\n", - "print('Notebook version: '+Notebook_version)\n", - "Latest_Notebook_version = All_notebook_versions[All_notebook_versions[\"Notebook\"] == Network]['Version'].iloc[0]\n", - "print('Latest notebook version: '+Latest_Notebook_version)\n", - "if Notebook_version == Latest_Notebook_version:\n", - " print(\"This notebook is up-to-date.\")\n", - "else:\n", - " print(bcolors.WARNING +\"A new version of this notebook has been released. We recommend that you download it at https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki\")\n", - "\n", - "\n", - "!pip freeze > requirements.txt\n", - "\n", - "#Create a pdf document with training summary\n", - "\n", - "def pdf_export(trained = False, augmentation = False, pretrained_model = False):\n", - " # save FPDF() class into a\n", - " # variable pdf\n", - " #from datetime import datetime\n", - "\n", - " class MyFPDF(FPDF, HTMLMixin):\n", - " pass\n", - "\n", - " pdf = MyFPDF()\n", - " pdf.add_page()\n", - " pdf.set_right_margin(-1)\n", - " pdf.set_font(\"Arial\", size = 11, style='B')\n", - "\n", - "\n", - " day = datetime.now()\n", - " datetime_str = str(day)[0:10]\n", - "\n", - " Header = 'Training report for '+Network+' model ('+model_name+')\\nDate: '+datetime_str\n", - " pdf.multi_cell(180, 5, txt = Header, align = 'L')\n", - "\n", - " # add another cell\n", - " if trained:\n", - " training_time = \"Training time: \"+str(hour)+ \"hour(s) \"+str(mins)+\"min(s) \"+str(round(sec))+\"sec(s)\"\n", - " pdf.cell(190, 5, txt = training_time, ln = 1, align='L')\n", - " pdf.ln(1)\n", - "\n", - " Header_2 = 'Information for your materials and methods:'\n", - " pdf.cell(190, 5, txt=Header_2, ln=1, align='L')\n", - "\n", - " all_packages = ''\n", - " for requirement in freeze(local_only=True):\n", - " all_packages = all_packages+requirement+', '\n", - " #print(all_packages)\n", - "\n", - " #Main Packages\n", - " main_packages = ''\n", - " version_numbers = []\n", - " for name in ['tensorflow','numpy','Keras','csbdeep']:\n", - " find_name=all_packages.find(name)\n", - " main_packages = main_packages+all_packages[find_name:all_packages.find(',',find_name)]+', '\n", - " #Version numbers only here:\n", - " version_numbers.append(all_packages[find_name+len(name)+2:all_packages.find(',',find_name)])\n", - "\n", - " try:\n", - " cuda_version = subprocess.run([\"nvcc\",\"--version\"],stdout=subprocess.PIPE)\n", - " cuda_version = cuda_version.stdout.decode('utf-8')\n", - " cuda_version = cuda_version[cuda_version.find(', V')+3:-1]\n", - " except:\n", - " cuda_version = ' - No cuda found - '\n", - " try:\n", - " gpu_name = subprocess.run([\"nvidia-smi\"],stdout=subprocess.PIPE)\n", - " gpu_name = gpu_name.stdout.decode('utf-8')\n", - " gpu_name = gpu_name[gpu_name.find('Tesla'):gpu_name.find('Tesla')+10]\n", - " except:\n", - " gpu_name = ' - No GPU found - '\n", - " #print(cuda_version[cuda_version.find(', V')+3:-1])\n", - " #print(gpu_name)\n", - "\n", - " shape = io.imread(Training_source+'/'+os.listdir(Training_source)[1]).shape\n", - " dataset_size = len(os.listdir(Training_source))\n", - "\n", - " text = 'The '+Network+' model was trained from scratch for '+str(number_of_epochs)+' epochs on '+str(dataset_size)+' paired image patches (image dimensions: '+str(shape)+', with a batch size of '+str(batch_size)+', using the '+Network+' ZeroCostDL4Mic notebook (v '+Notebook_version[0]+') (von Chamier & Laine et al., 2020). Key python packages used include tensorflow (v '+version_numbers[0]+'), Keras (v '+version_numbers[2]+'), numpy (v '+version_numbers[1]+'), cuda (v '+cuda_version+'). The training was accelerated using a '+gpu_name+'GPU.'\n", - "\n", - "\n", - " if pretrained_model:\n", - " text = 'The '+Network+' model was trained for '+str(number_of_epochs)+' epochs on '+str(dataset_size)+' paired image patches (image dimensions: '+str(shape)+', with a batch size of '+str(batch_size)+', using the '+Network+' ZeroCostDL4Mic notebook (v '+Notebook_version[0]+') (von Chamier & Laine et al., 2020). The model was re-trained from a pretrained model. Key python packages used include tensorflow (v '+version_numbers[0]+'), Keras (v '+version_numbers[2]+'), numpy (v '+version_numbers[1]+'), cuda (v '+cuda_version+'). The training was accelerated using a '+gpu_name+'GPU.'\n", - "\n", - " # print(text)\n", - "\n", - " pdf.set_font('')\n", - " pdf.set_font_size(10.)\n", - " pdf.multi_cell(190, 5, txt = text, align='L')\n", - " pdf.set_font('')\n", - " pdf.set_font('Arial', size = 10, style = 'B')\n", - " pdf.ln(1)\n", - " pdf.cell(28, 5, txt='Augmentation: ', ln=0)\n", - " pdf.set_font('')\n", - " if augmentation:\n", - " aug_text = 'The dataset was augmented by a factor of '+str(Multiply_dataset_by)\n", - "\n", - " else:\n", - " aug_text = 'No augmentation was used for training.'\n", - " pdf.multi_cell(190, 5, txt=aug_text, align='L')\n", - " pdf.set_font('Arial', size = 11, style = 'B')\n", - " pdf.ln(1)\n", - " pdf.cell(180, 5, txt = 'Parameters', align='L', ln=1)\n", - " pdf.set_font('')\n", - " pdf.set_font_size(10.)\n", - " if Use_Default_Advanced_Parameters:\n", - " pdf.cell(200, 5, txt='Default Advanced Parameters were enabled')\n", - " pdf.cell(200, 5, txt='The following parameters were used for training:')\n", - " pdf.ln(1)\n", - " html = \"\"\"\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ParameterValue
number_of_epochs{0}
batch_size{1}
percentage_validation{2}
initial_learning_rate{3}
\n", - " \"\"\".format(number_of_epochs,batch_size,percentage_validation,initial_learning_rate)\n", - " pdf.write_html(html)\n", - "\n", - " #pdf.multi_cell(190, 5, txt = text_2, align='L')\n", - " pdf.set_font(\"Arial\", size = 11, style='B')\n", - " pdf.ln(1)\n", - " pdf.cell(190, 5, txt = 'Training Dataset', align='L', ln=1)\n", - " pdf.set_font('')\n", - " pdf.set_font('Arial', size = 10, style = 'B')\n", - " pdf.cell(29, 5, txt= 'Training_source:', align = 'L', ln=0)\n", - " pdf.set_font('')\n", - " pdf.multi_cell(170, 5, txt = Training_source, align = 'L')\n", - " pdf.set_font('')\n", - " pdf.set_font('Arial', size = 10, style = 'B')\n", - " pdf.cell(27, 5, txt= 'Training_target:', align = 'L', ln=0)\n", - " pdf.set_font('')\n", - " pdf.multi_cell(170, 5, txt = Training_target, align = 'L')\n", - " #pdf.cell(190, 5, txt=aug_text, align='L', ln=1)\n", - " pdf.ln(1)\n", - " pdf.set_font('')\n", - " pdf.set_font('Arial', size = 10, style = 'B')\n", - " pdf.cell(22, 5, txt= 'Model Path:', align = 'L', ln=0)\n", - " pdf.set_font('')\n", - " pdf.multi_cell(170, 5, txt = model_path+'/'+model_name, align = 'L')\n", - " pdf.ln(1)\n", - " pdf.cell(60, 5, txt = 'Example Training pair', ln=1)\n", - " pdf.ln(1)\n", - " exp_size = io.imread(base_path + '/TrainingDataExample_Cellpose2D.png').shape\n", - " pdf.image(base_path + '/TrainingDataExample_Cellpose2D.png', x = 11, y = None, w = round(exp_size[1]/8), h = round(exp_size[0]/8))\n", - " pdf.ln(1)\n", - " ref_1 = 'References:\\n - ZeroCostDL4Mic: von Chamier, Lucas & Laine, Romain, et al. \"ZeroCostDL4Mic: an open platform to simplify access and use of Deep-Learning in Microscopy.\" BioRxiv (2020).'\n", - " pdf.multi_cell(190, 5, txt = ref_1, align='L')\n", - " ref_2 = '- Cellpose: Stringer, Carsen, et al. \"Cellpose: a generalist algorithm for cellular segmentation.\" Nature Methods 18, pages100-106(2021).'\n", - " pdf.multi_cell(190, 5, txt = ref_2, align='L')\n", - " pdf.ln(3)\n", - " reminder = 'Important:\\nRemember to perform the quality control step on all newly trained models\\nPlease consider depositing your training dataset on Zenodo'\n", - " pdf.set_font('')\n", - " pdf.set_font('Arial', size = 11, style='B')\n", - " pdf.multi_cell(190, 5, txt=reminder, align='C')\n", - "\n", - " pdf.output(model_path+'/'+model_name+'/'+model_name+\"_training_report.pdf\")\n", - " # pdf.output(Saving_path+'/train_folder/models/'+model_name+\"_training_report.pdf\")\n", - "\n", - "\n", - "\n", - "#Make a pdf summary of the QC results\n", - "\n", - "def qc_pdf_export():\n", - " class MyFPDF(FPDF, HTMLMixin):\n", - " pass\n", - "\n", - " pdf = MyFPDF()\n", - " pdf.add_page()\n", - " pdf.set_right_margin(-1)\n", - " pdf.set_font('')\n", - " pdf.set_font(\"Arial\", size = 11, style='B')\n", - "\n", - " Network = 'Cellpose 2D'\n", - " #model_name = os.path.basename(full_QC_model_path)\n", - " day = datetime.now()\n", - " datetime_str = str(day)[0:10]\n", - "\n", - " Header = 'Quality Control report for '+Network+' model ('+QC_model_name+')\\nDate: '+datetime_str\n", - " pdf.multi_cell(180, 5, txt = Header, align = 'L')\n", - "\n", - " all_packages = ''\n", - " for requirement in freeze(local_only=True):\n", - " all_packages = all_packages+requirement+', '\n", - "\n", - " pdf.set_font('')\n", - " pdf.set_font('Arial', size = 11, style = 'B')\n", - " pdf.ln(2)\n", - " pdf.cell(190, 5, txt = 'Development of Training Losses', ln=1, align='L')\n", - " pdf.ln(1)\n", - " exp_size = io.imread(full_QC_model_path+'Quality Control/QC_example_data.png').shape\n", - " if os.path.exists(full_QC_model_path+'Quality Control/lossCurvePlots.png'):\n", - " pdf.image(full_QC_model_path+'Quality Control/lossCurvePlots.png', x = 11, y = None, w = round(exp_size[1]/10), h = round(exp_size[0]/13))\n", - " else:\n", - " pdf.set_font('')\n", - " pdf.set_font('Arial', size=10)\n", - " pdf.multi_cell(190, 5, txt='If you would like to see the evolution of the loss function during training please play the first cell of the QC section in the notebook.', align='L')\n", - " pdf.ln(2)\n", - " pdf.set_font('')\n", - " pdf.set_font('Arial', size = 10, style = 'B')\n", - " pdf.ln(3)\n", - " pdf.cell(80, 5, txt = 'Example Quality Control Visualisation', ln=1)\n", - " pdf.ln(1)\n", - " exp_size = io.imread(full_QC_model_path+'Quality Control/QC_example_data.png').shape\n", - " pdf.image(full_QC_model_path+'Quality Control/QC_example_data.png', x = 16, y = None, w = round(exp_size[1]/10), h = round(exp_size[0]/10))\n", - " pdf.ln(1)\n", - " pdf.set_font('')\n", - " pdf.set_font('Arial', size = 11, style = 'B')\n", - " pdf.ln(1)\n", - " pdf.cell(180, 5, txt = 'Quality Control Metrics', align='L', ln=1)\n", - " pdf.set_font('')\n", - " pdf.set_font_size(10.)\n", - "\n", - " pdf.ln(1)\n", - " html = \"\"\"\n", - " \n", - " \n", - " \"\"\"\n", - " with open(full_QC_model_path+'Quality Control/Quality_Control for '+QC_model_name+'.csv', 'r') as csvfile:\n", - " metrics = csv.reader(csvfile)\n", - " header = next(metrics)\n", - " image = header[0]\n", - " mSSIM_PvsGT = header[1]\n", - " mSSIM_SvsGT = header[2]\n", - " NRMSE_PvsGT = header[3]\n", - " NRMSE_SvsGT = header[4]\n", - " PSNR_PvsGT = header[5]\n", - " PSNR_SvsGT = header[6]\n", - " header = \"\"\"\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \"\"\".format(image,mSSIM_PvsGT,mSSIM_SvsGT,NRMSE_PvsGT,NRMSE_SvsGT,PSNR_PvsGT,PSNR_SvsGT)\n", - " html = html+header\n", - " for row in metrics:\n", - " image = row[0]\n", - " mSSIM_PvsGT = row[1]\n", - " mSSIM_SvsGT = row[2]\n", - " NRMSE_PvsGT = row[3]\n", - " NRMSE_SvsGT = row[4]\n", - " PSNR_PvsGT = row[5]\n", - " PSNR_SvsGT = row[6]\n", - " cells = \"\"\"\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \"\"\".format(image,str(round(float(mSSIM_PvsGT),3)),str(round(float(mSSIM_SvsGT),3)),str(round(float(NRMSE_PvsGT),3)),str(round(float(NRMSE_SvsGT),3)),str(round(float(PSNR_PvsGT),3)),str(round(float(PSNR_SvsGT),3)))\n", - " html = html+cells\n", - " html = html+\"\"\"
{0}{1}{2}{3}{4}{5}{6}
{0}{1}{2}{3}{4}{5}{6}
\"\"\"\n", - "\n", - " pdf.write_html(html)\n", - "\n", - " pdf.ln(1)\n", - " pdf.set_font('')\n", - " pdf.set_font_size(10.)\n", - " ref_1 = 'References:\\n - ZeroCostDL4Mic: von Chamier, Lucas & Laine, Romain, et al. \"ZeroCostDL4Mic: an open platform to simplify access and use of Deep-Learning in Microscopy.\" BioRxiv (2020).'\n", - " pdf.multi_cell(190, 5, txt = ref_1, align='L')\n", - " ref_2 = '- CARE: Weigert, Martin, et al. \"Content-aware image restoration: pushing the limits of fluorescence microscopy.\" Nature methods 15.12 (2018): 1090-1097.'\n", - " pdf.multi_cell(190, 5, txt = ref_2, align='L')\n", - "\n", - " pdf.ln(3)\n", - " reminder = 'To find the parameters and other information about how this model was trained, go to the training_report.pdf of this model which should be in the folder of the same name.'\n", - "\n", - " pdf.set_font('Arial', size = 11, style='B')\n", - " pdf.multi_cell(190, 5, txt=reminder, align='C')\n", - "\n", - " pdf.output(full_QC_model_path+'Quality Control/'+QC_model_name+'_QC_report.pdf')\n", - "\n", - "\n", - "# Build requirements file for local run\n", - "after = [str(m) for m in sys.modules]\n", - "build_requirements_file(before, after)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "b4-r1gE7Iamv" - }, - "source": [ - "# **2. Initialise the Colab session**\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "DMNHVZfHmbKb" - }, - "source": [ - "\n", - "## **2.1. Check for GPU access**\n", - "---\n", - "\n", - "By default, the session should be using Python 3 and GPU acceleration, but it is possible to ensure that these are set properly by doing the following:\n", - "\n", - "Go to **Runtime -> Change the Runtime type**\n", - "\n", - "**Runtime type: Python 3** *(Python 3 is programming language in which this program is written)*\n", - "\n", - "**Accelerator: GPU** *(Graphics processing unit)*\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "id": "BDhmUgqCStlm" - }, - "outputs": [], - "source": [ - "#@markdown ##Run this cell to check if you have GPU access\n", - "\n", - "#%tensorflow_version 1.x\n", - "\n", - "import tensorflow as tf\n", - "if tf.test.gpu_device_name()=='':\n", - " print('You do not have GPU access.')\n", - " print('Did you change your runtime ?')\n", - " print('If the runtime setting is correct then Google did not allocate a GPU for your session')\n", - " print('Expect slow performance. To access GPU try reconnecting later')\n", - "\n", - "else:\n", - " print('You have GPU access')\n", - " !nvidia-smi" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "-oqBTeLaImnU" - }, - "source": [ - "## **2.2. Mount your Google Drive**\n", - "---\n", - " To use this notebook on the data present in your Google Drive, you need to mount your Google Drive to this notebook.\n", - "\n", - " Play the cell below to mount your Google Drive and follow the instructions.\n", - "\n", - " Once this is done, your data are available in the **Files** tab on the top left of notebook." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "id": "01Djr8v-5pPk" - }, - "outputs": [], - "source": [ - "\n", - "#@markdown ##Run this cell to connect your Google Drive to Colab\n", - "\n", - "#@markdown * Follow the instructions.\n", - "\n", - "\n", - "#@markdown * Click on \"Files\" site on the right. Refresh the site. Your Google Drive folder should now be available here as \"drive\".\n", - "\n", - "#mounts user's Google Drive to Google Colab.\n", - "\n", - "from google.colab import drive\n", - "drive.mount('/content/gdrive')\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "2UfUWjI_askO" - }, - "source": [ - "# **3. Select your parameters and paths**\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ZyMxrSWvavVL" - }, - "source": [ - "## **3.1. Setting main training parameters**\n", - "---\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "5MlTyQVXXvDx" - }, - "source": [ - " **Paths for training, predictions and results**\n", - "\n", - "\n", - "**`Training_source:`, `Training_target`:** These are the paths to your folders containing the Training_source (images of cells) and Training_target (masks) training data respecively. To find the paths of the folders containing the respective datasets, go to your Files on the left of the notebook, navigate to the folder containing your files and copy the path by right-clicking on the folder, **Copy path** and pasting it into the right box below.\n", - "\n", - "**`model_name`:** Use only my_model -style, not my-model (Use \"_\" not \"-\"). Do not use spaces in the name. Avoid using the name of an existing model (saved in the same folder) as it will be overwritten.\n", - "\n", - "**`model_path`**: Enter the path where your model will be saved once trained (for instance your result folder).\n", - "\n", - "\n", - "**Training parameters**\n", - "\n", - "**`number_of_epochs`:** Input how many epochs (rounds) the network will be trained. Preliminary results can already be observed after a 100 epochs, but a full training should run for up to 500-1000 epochs. Evaluate the performance after training (see 5.). **Default value: 500**\n", - "\n", - "**Advanced Parameters - experienced users only**\n", - "\n", - "**`batch_size:`** This parameter defines the number of patches seen in each training step. Reducing or increasing the **batch size** may slow or speed up your training, respectively, and can influence network performance. **Default value: 8**\n", - "\n", - "\n", - "**`percentage_validation`:** Input the percentage of your training dataset you want to use to validate the network during the training. **Default value: 10**\n", - "\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "id": "2HkNZ16BdfJv" - }, - "outputs": [], - "source": [ - "#@markdown ###Path to training images:\n", - "\n", - "Training_source = \"\" #@param {type:\"string\"}\n", - "Training_target = \"\" #@param {type:\"string\"}\n", - "\n", - "#Define where the patch file will be saved\n", - "base = base_path\n", - "\n", - "# model name and path\n", - "#@markdown ###Name of the model and path to model folder:\n", - "model_name = \"\" #@param {type:\"string\"}\n", - "model_path = \"\" #@param {type:\"string\"}\n", - "\n", - "# other parameters for training.\n", - "#@markdown ###Training Parameters:\n", - "#@markdown Number of epochs:\n", - "number_of_epochs = 500#@param {type:\"number\"}\n", - "\n", - "Channel_to_use_for_training = \"Red\" #@param [\"Grayscale\", \"Blue\", \"Green\", \"Red\"]\n", - "\n", - "# @markdown ###If you have a secondary channel that can be used for training, for instance nuclei, choose it here:\n", - "\n", - "Second_training_channel= \"None\" #@param [\"None\", \"Blue\", \"Green\", \"Red\"]\n", - "\n", - "\n", - "#@markdown ###Advanced Parameters\n", - "\n", - "Use_Default_Advanced_Parameters = True #@param {type:\"boolean\"}\n", - "#@markdown ###If not, please input:\n", - "batch_size = 8#@param {type:\"number\"}\n", - "initial_learning_rate = 0.0002 #@param {type:\"number\"}\n", - "percentage_validation = 10#@param {type:\"number\"}\n", - "\n", - "if (Use_Default_Advanced_Parameters):\n", - " print(\"Default advanced parameters enabled\")\n", - " batch_size = 8\n", - " initial_learning_rate = 0.0002\n", - " percentage_validation = 10\n", - "\n", - "#here we check that no model with the same name already exist, if so delete\n", - "if os.path.exists(model_path+'/'+model_name):\n", - " print(bcolors.WARNING +\"!! WARNING: \"+model_name+\" already exists and will be deleted in the following cell !!\")\n", - " print(bcolors.WARNING +\"To continue training \"+model_name+\", choose a new model_name here, and load \"+model_name+\" in section 3.3\"+W)\n", - "\n", - "# Here we enable the cyto pre-trained model by default (in case the cell is not ran)\n", - "model_to_load = \"cyto\"\n", - "# Here we disable data augmentation by default (in case the cell is not ran)\n", - "\n", - "Use_Data_augmentation = True\n", - "\n", - "# This will display a randomly chosen dataset input and output\n", - "random_choice = random.choice(os.listdir(Training_source))\n", - "x = io.imread(Training_source+\"/\"+random_choice)\n", - "norm = simple_norm(x, percent = 99)\n", - "y = io.imread(Training_target+\"/\"+random_choice)\n", - "\n", - "# Find the number of channel in the input image\n", - "\n", - "n_channel = 1 if x.ndim == 2 else x.shape[-1]\n", - "\n", - "\n", - "# Here we match the channel to number\n", - "\n", - "if Channel_to_use_for_training == \"Grayscale\":\n", - " Training_channel = 0\n", - "\n", - " if not n_channel == 1:\n", - " print(bcolors.WARNING +\"!! WARNING: your image has more than one channel, choose which channel you want to use for trainning !!\")\n", - "\n", - "if Channel_to_use_for_training == \"Blue\":\n", - " Training_channel = 3\n", - "\n", - "if Channel_to_use_for_training == \"Green\":\n", - " Training_channel = 2\n", - "\n", - "if Channel_to_use_for_training == \"Red\":\n", - " Training_channel = 1\n", - "\n", - "\n", - "if Second_training_channel == \"Blue\":\n", - " Second_training_channel = 3\n", - "\n", - "if Second_training_channel == \"Green\":\n", - " Second_training_channel = 2\n", - "\n", - "if Second_training_channel == \"Red\":\n", - " Second_training_channel = 1\n", - "\n", - "if Second_training_channel == \"None\":\n", - " Second_training_channel = 0\n", - "\n", - "\n", - "if n_channel ==1:\n", - "\n", - " f=plt.figure(figsize=(16,8))\n", - " plt.subplot(1,2,1)\n", - " plt.imshow(x, norm=norm, cmap='magma', interpolation='nearest')\n", - " plt.title('Training source')\n", - " plt.axis('off');\n", - "\n", - " plt.subplot(1,2,2)\n", - " plt.imshow(y,cmap='nipy_spectral', interpolation='nearest')\n", - " plt.title('Training target')\n", - " plt.axis('off');\n", - "\n", - " plt.savefig(base_path + '/TrainingDataExample_Cellpose2D.png',bbox_inches='tight',pad_inches=0)\n", - "\n", - "else:\n", - "\n", - " f=plt.figure(figsize=(20,10))\n", - " plt.subplot(1,3,1)\n", - " plt.imshow(x, interpolation='nearest')\n", - " plt.title('Training source')\n", - " plt.axis('off');\n", - "\n", - " plt.subplot(1,3,2)\n", - " plt.imshow(x[:, :, int(Training_channel-1)],cmap='magma', interpolation='nearest')\n", - " plt.title('Channel used for training')\n", - " plt.axis('off');\n", - "\n", - " plt.subplot(1,3,3)\n", - " plt.imshow(y,cmap='nipy_spectral', interpolation='nearest')\n", - " plt.title('Training target')\n", - " plt.axis('off');\n", - "\n", - " plt.savefig(base_path + '/TrainingDataExample_Cellpose2D.png',bbox_inches='tight',pad_inches=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "qEg6ar0PhuDY" - }, - "source": [ - "## **3.2. Data augmentation**\n", - "---\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "t6q9aqDUhxlw" - }, - "source": [ - "Data augmentation can improve training progress by amplifying differences in the dataset. This can be useful if the available dataset is small since, in this case, it is possible that a network could quickly learn every example in the dataset (overfitting), without augmentation. Augmentation is not necessary for training and if your training dataset is large you should disable it.\n", - "\n", - "By default, a x4 data augmentation is enabled in this notebook." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "id": "SblwpgmahfBl" - }, - "outputs": [], - "source": [ - "#Data augmentation\n", - "\n", - "Use_Data_augmentation = False #@param {type:\"boolean\"}\n", - "\n", - "if Use_Data_augmentation:\n", - " print(bcolors.WARNING+\"Data augmentation enabled\")\n", - " Multiply_dataset_by = 4\n", - "\n", - "\n", - "if not Use_Data_augmentation:\n", - " print(bcolors.WARNING+\"Data augmentation disabled\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s2NC_-Tuc02W" - }, - "source": [ - "\n", - "## **3.3. Using weights from a pre-trained model as initial weights**\n", - "---\n", - " Here, you can set the path to a pre-trained model from which the weights can be extracted and used as a starting point for this training session. **This pre-trained model needs to be a Cellpose model**.\n", - "\n", - " You can also use the pretrained models already available in Cellpose:\n", - "\n", - "- The cytoplasm model in cellpose is trained on two-channel images, where the first channel is the channel to segment, and the second channel is an optional nuclear channel.\n", - "\n", - "- The cytoplasm2 model is an updated cytoplasm model trained with user-submitted images.\n", - "\n", - "- The nuclear model in cellpose is trained on two-channel images, where the first channel is the channel to segment, and the second channel is always set to an array of zeros.\n", - "\n", - " This option allows you to perform training over multiple Colab runtimes or to do transfer learning using models trained outside of ZeroCostDL4Mic. **You do not need to run this section if you want to train a network from scratch**.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "id": "sLdgQM6Rc7vp" - }, - "outputs": [], - "source": [ - "# @markdown ##Loading weights from a pre-trained network\n", - "\n", - "Use_pretrained_model = True #@param {type:\"boolean\"}\n", - "\n", - "Pretrained_model = \"Cytoplasm3\" #@param [\"Cytoplasm\",\"Cytoplasm2\", \"Cytoplasm3\", \"Nuclei\", \"LiveCell\", \"TissueNet\", \"Own_model\"]\n", - "\n", - "#@markdown ###If using your own model, please provide the path to the model (not the folder):\n", - "pretrained_model_path = \"\" #@param {type:\"string\"}\n", - "\n", - "# --------------------- Check if we load a previously trained model ------------------------\n", - "\n", - "if Use_pretrained_model == True :\n", - "\n", - " if Pretrained_model == \"Own_model\":\n", - "\n", - " model_to_load = pretrained_model_path\n", - "\n", - " print('The model '+ str(model_to_load) + \"will be used as a starting point\")\n", - "\n", - " if Pretrained_model == \"Cytoplasm\":\n", - " model_to_load = \"cyto\"\n", - " print('The model Cytoplasm will be used as a starting point')\n", - "\n", - " if Pretrained_model == \"Cytoplasm2\":\n", - " model_to_load = \"cyto2\"\n", - " print('The model Cytoplasm2 (cyto2) will be used as a starting point')\n", - "\n", - " if Pretrained_model == \"Cytoplasm3\":\n", - " model_to_load = \"cyto3\"\n", - " print('The model Cytoplasm3 (cyto3) will be used as a starting point')\n", - "\n", - " if Pretrained_model == \"Nuclei\":\n", - " model_to_load = \"nuclei\"\n", - " print('The model nuclei will be used as a starting point')\n", - "\n", - " if Pretrained_model == \"LiveCell\":\n", - " model_to_load = \"livecell\"\n", - " print('The model LiveCell will be used as a starting point')\n", - "\n", - " if Pretrained_model == \"TissueNet\":\n", - " model_to_load = \"tissuenet\"\n", - " print('The model TissueNet will be used as a starting point')\n", - "\n", - "else:\n", - " model_to_load = None\n", - " print(bcolors.WARNING+'No pretrained network will be used.')\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "qeYZ7PeValfs" - }, - "source": [ - "# **4. Train the network**\n", - "---" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "id": "tsn8WV3Wl0sG" - }, - "outputs": [], - "source": [ - "#@markdown ##Create the model and dataset objects\n", - "\n", - "\n", - "# Here we check that the model destination folder is empty\n", - "if os.path.exists(model_path+'/'+model_name):\n", - " print(bcolors.WARNING +\"!! WARNING: Model folder already exists and has been removed !!\" + W)\n", - " shutil.rmtree(model_path+'/'+model_name)\n", - "\n", - "os.makedirs(model_path+\"/\"+model_name)\n", - "\n", - "\n", - "#To use cellpose to work we need to organise the data in a way the network can understand\n", - "\n", - "# Here we count the number of files in the training target folder\n", - "Filelist = os.listdir(Training_target)\n", - "number_files = len(Filelist)\n", - "\n", - "# Here we count the number of file to use for validation\n", - "Image_for_validation = int((number_files)*(percentage_validation/100))\n", - "\n", - "if Image_for_validation == 0:\n", - " Image_for_validation = 1\n", - "\n", - "Saving_path = base_path + \"/\" + model_name\n", - "\n", - "if os.path.exists(Saving_path):\n", - " shutil.rmtree(Saving_path)\n", - "os.makedirs(Saving_path)\n", - "\n", - "train_folder = Saving_path+\"/train_folder\"\n", - "os.makedirs(train_folder)\n", - "\n", - "test_folder = Saving_path+\"/test_folder\"\n", - "os.makedirs(test_folder)\n", - "\n", - "index = 0\n", - "\n", - "print('Copying training source data...')\n", - "for f in tqdm(os.listdir(Training_source)):\n", - " short_name = os.path.splitext(f)\n", - "\n", - " if index < Image_for_validation:\n", - " shutil.copyfile(Training_source+\"/\"+f, test_folder+\"/\"+short_name[0]+\"_img.tif\")\n", - " shutil.copyfile(Training_target+\"/\"+f, test_folder+\"/\"+short_name[0]+\"_masks.tif\")\n", - " else:\n", - " shutil.copyfile(Training_source+\"/\"+f, train_folder+\"/\"+short_name[0]+\"_img.tif\")\n", - " shutil.copyfile(Training_target+\"/\"+f, train_folder+\"/\"+short_name[0]+\"_masks.tif\")\n", - " index = index +1\n", - "\n", - "print(\"Done\")\n", - "\n", - "pdf_export(augmentation = Use_Data_augmentation, pretrained_model = Use_pretrained_model)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "jRorFe296LgI" - }, - "source": [ - "## **4.2. Start Training**\n", - "---\n", - "\n", - "When playing the cell below you should see updates after each epoch (round). Network training can take some time.\n", - "\n", - "* **CRITICAL NOTE:** Training is currently done using Torch.\n", - "\n", - "* **CRITICAL NOTE:** Google Colab has a time limit for processing (to prevent using GPU power for datamining). Training time must be less than 12 hours! If training takes longer than 12 hours, please decrease the number of epochs or number of patches. Another way circumvent this is to save the parameters of the model after training and start training again from this point.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "id": "YXUnd3awi6K3" - }, - "outputs": [], - "source": [ - "#@markdown ##Start training\n", - "\n", - "start = time.time()\n", - "\n", - "if not Use_Data_augmentation:\n", - " !python -m cellpose --train --use_gpu --dir \"$train_folder\" --test_dir \"$test_folder\" --pretrained_model $model_to_load --chan $Training_channel --chan2 $Second_training_channel --n_epochs $number_of_epochs --learning_rate $initial_learning_rate --batch_size $batch_size --img_filter img --mask_filter masks --verbose\n", - "\n", - "else:\n", - " !python -m cellpose --train --use_gpu --dir \"$train_folder\" --test_dir \"$test_folder\" --pretrained_model $model_to_load --chan $Training_channel --chan2 $Second_training_channel --n_epochs $number_of_epochs --learning_rate $initial_learning_rate --batch_size $batch_size --img_filter img --mask_filter masks --verbose\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "#Here we copy the model to the result folder after training\n", - "\n", - "if os.path.exists(model_path+'/'+model_name):\n", - " print(bcolors.WARNING +\"!! WARNING: Model folder already exists and has been removed !!\" + W)\n", - " shutil.rmtree(model_path+'/'+model_name)\n", - "destination = shutil.copytree(Saving_path+\"/train_folder/models\", model_path+\"/\"+model_name)\n", - "\n", - "# Displaying the time elapsed for training\n", - "dt = time.time() - start\n", - "mins, sec = divmod(dt, 60)\n", - "hour, mins = divmod(mins, 60)\n", - "print(\"Time elapsed:\",hour, \"hour(s)\",mins,\"min(s)\",round(sec),\"sec(s)\")\n", - "print(\"Your model is also available here: \"+str(model_path+\"/\"+model_name))\n", - "\n", - "\n", - "pdf_export(trained=True, augmentation = Use_Data_augmentation, pretrained_model = Use_pretrained_model)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "qvbm9EJGaXr9" - }, - "source": [ - "# **5. Evaluate your model**\n", - "---\n", - "\n", - "This section allows the user to perform important quality checks on the validity and generalisability of the trained model. \n", - "\n", - "\n", - "**We highly recommend to perform quality control on all newly trained models.**\n", - "\n", - "**`model_choice`:** Choose the model to use to make predictions. This model needs to be a Cellpose model. You can also use the pretrained models already available in cellpose:\n", - "\n", - "- The cytoplasm model in cellpose is trained on two-channel images, where the first channel is the channel to segment, and the second channel is an optional nuclear channel.\n", - "- The cytoplasm2 and 3 models are updated cytoplasm model trained with user-submitted images.\n", - "\n", - "- The Nuclei model in cellpose is trained on two-channel images, where the first channel is the channel to segment, and the second channel is always set to an array of zeros.\n", - "\n", - "- The 'livecell' model was trained on all training images from the [livecell dataset](https://sartorius-research.github.io/LIVECell/). These images only have a cytoplasm channel.\n", - "\n", - "- The 'tissuenet' model was trained on all training images from the [tissuenet dataset](https://datasets.deepcell.org/). These images have a cytoplasm channel and a nuclear channel\n", - "\n", - "- The bacteria Omnipose model is an Omnipose model dedicated to the segmentation of bacteria or other elongated cells.\n", - "\n", - "- The cytoplasm2 Omnipose model is an Omnipose model trained using the same dataset as the cellpose cytoplasm2 model.\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "id": "IeiU6D2jGDh4" - }, - "outputs": [], - "source": [ - "# model name and path\n", - "#@markdown ###Do you want to assess the model you just trained ?\n", - "Use_the_current_trained_model = True #@param {type:\"boolean\"}\n", - "\n", - "#@markdown ###If not, indicate which model you want to assess:\n", - "\n", - "QC_model = \"Own_model\" #@param [\"Cytoplasm\",\"Cytoplasm2\", \"Cytoplasm3\", \"LiveCell\", \"TissueNet\", \"Cytoplasm2_Omnipose\", \"Bacteria_Omnipose\", \"Nuclei\", \"Own_model\"]\n", - "\n", - "#@markdown ###If using your own model, please provide the path to the model (not the folder):\n", - "\n", - "QC_model_path = \"\" #@param {type:\"string\"}\n", - "\n", - "#@markdown ###If using the Cellpose or Omnipose models, please indicate where you want to save the results:\n", - "Saving_path = \"\" #@param {type:\"string\"}\n", - "\n", - "\n", - "if Use_the_current_trained_model :\n", - "\n", - " list_files = sorted([i for i in os.listdir(model_path+\"/\"+model_name) if not i.endswith('.pdf')])\n", - "\n", - " QC_model_path = model_path+\"/\"+model_name+\"/\"+list_files[0]\n", - " QC_model = \"Own_model\"\n", - "\n", - " #model = models.CellposeModel(gpu=True, pretrained_model=QC_model_path, torch=False, diam_mean=30.0, net_avg=True, device=None, residual_on=True, style_on=True, concatenation=False)\n", - " #model = models.CellposeModel(gpu=True, pretrained_model=QC_model_path, diam_mean=30.0, net_avg=True, device=None, residual_on=True, style_on=True, concatenation=False)\n", - " model = models.CellposeModel(gpu=True, pretrained_model=QC_model_path)\n", - "\n", - " QC_model_folder = os.path.dirname(QC_model_path)\n", - " QC_model_name = os.path.basename(QC_model_folder)\n", - " Saving_path = QC_model_folder\n", - "\n", - " print(\"The \"+str(QC_model_name)+\" model will be evaluated\")\n", - "\n", - "if not Use_the_current_trained_model:\n", - "\n", - " if QC_model == \"Cytoplasm\":\n", - " model = models.Cellpose(gpu=True, model_type=\"cyto\")\n", - " QC_model_folder = Saving_path\n", - " QC_model_name = \"Cytoplasm\"\n", - "\n", - " print('The model \"Cytoplasm\" will be evaluated')\n", - "\n", - " if QC_model == \"Cytoplasm2\":\n", - " model = models.Cellpose(gpu=True, model_type=\"cyto2\")\n", - " QC_model_folder = Saving_path\n", - " QC_model_name = \"Cytoplasm2\"\n", - "\n", - " print('The model \"Cytoplasm2\" will be evaluated')\n", - "\n", - " if model_choice == \"Cytoplasm3\":\n", - " channels=[segment_channel,nuclear_channel]\n", - " model = models.Cellpose(gpu=True, model_type=\"cyto3\")\n", - " print(\"Cytoplasm 3 model enabled\")\n", - "\n", - " if QC_model == \"Cytoplasm2_Omnipose\":\n", - " model = models.Cellpose(gpu=True, model_type=\"cyto2_omni\")\n", - " QC_model_folder = Saving_path\n", - " QC_model_name = \"Cytoplasm2_Omnipose\"\n", - " print(\"The model Cytoplasm2_Omnipose will be evaluated\")\n", - "\n", - " if QC_model == \"LiveCell\":\n", - " channels=[segment_channel,0]\n", - " model = models.CellposeModel(gpu=True, model_type=\"livecell\")\n", - " print(\"LiveCell model enabled\")\n", - "\n", - " if QC_model == \"Bacteria_Omnipose\":\n", - " channels=[segment_channel,nuclear_channel]\n", - " model = models.Cellpose(gpu=True, model_type=\"bact_omni\")\n", - " Object_diameter = 0\n", - " print(\"Bacteria_omnipose model enabled\")\n", - "\n", - " if QC_model == \"TissueNet\":\n", - " channels=[segment_channel,nuclear_channel]\n", - " model = models.CellposeModel(gpu=True, model_type='tissuenet')\n", - " print(\"TissueNet model enabled\")\n", - "\n", - " if QC_model == \"Nuclei\":\n", - " model = models.Cellpose(gpu=True, model_type=\"nuclei\")\n", - "\n", - " QC_model_folder = Saving_path\n", - " QC_model_name = \"Nuclei\"\n", - "\n", - " print('The model \"Nuclei\" will be evaluated')\n", - "\n", - " if QC_model == \"Own_model\":\n", - "\n", - " if os.path.exists(QC_model_path):\n", - " model = models.CellposeModel(gpu=True, pretrained_model=QC_model_path, torch=True, diam_mean=30.0, net_avg=True, device=None, residual_on=True, style_on=True, concatenation=False)\n", - "\n", - " QC_model_folder = os.path.dirname(QC_model_path)\n", - " Saving_path = QC_model_folder\n", - " QC_model_name = os.path.basename(QC_model_folder)\n", - " print(\"The \"+str(QC_model_name)+\" model will be evaluated\")\n", - "\n", - " else:\n", - " print(bcolors.WARNING+'!! WARNING: The chosen model does not exist !!')\n", - " print('Please make sure you provide a valid model path and model name before proceeding further.')\n", - "\n", - "#Here we make the folder to save the resuslts if it does not exists\n", - "\n", - "if not Saving_path == \"\":\n", - " if os.path.exists(QC_model_folder) == False:\n", - " os.makedirs(QC_model_folder)\n", - "else:\n", - " print(bcolors.WARNING+'!! WARNING: Indicate where you want to save the results')\n", - "\n", - "\n", - "# Here we load the def that perform the QC, code taken from StarDist https://github.com/mpicbg-csbd/stardist/blob/master/stardist/matching.py\n", - "\n", - "import numpy as np\n", - "from numba import jit\n", - "from tqdm import tqdm\n", - "from scipy.optimize import linear_sum_assignment\n", - "from collections import namedtuple\n", - "\n", - "\n", - "matching_criteria = dict()\n", - "\n", - "def label_are_sequential(y):\n", - " \"\"\" returns true if y has only sequential labels from 1... \"\"\"\n", - " labels = np.unique(y)\n", - " return (set(labels)-{0}) == set(range(1,1+labels.max()))\n", - "\n", - "\n", - "def is_array_of_integers(y):\n", - " return isinstance(y,np.ndarray) and np.issubdtype(y.dtype, np.integer)\n", - "\n", - "\n", - "def _check_label_array(y, name=None, check_sequential=False):\n", - " err = ValueError(\"{label} must be an array of {integers}.\".format(\n", - " label = 'labels' if name is None else name,\n", - " integers = ('sequential ' if check_sequential else '') + 'non-negative integers',\n", - " ))\n", - " is_array_of_integers(y) or print(\"An error occured\")\n", - " if check_sequential:\n", - " label_are_sequential(y) or print(\"An error occured\")\n", - " else:\n", - " y.min() >= 0 or print(\"An error occured\")\n", - " return True\n", - "\n", - "\n", - "def label_overlap(x, y, check=True):\n", - " if check:\n", - " _check_label_array(x,'x',True)\n", - " _check_label_array(y,'y',True)\n", - " x.shape == y.shape or _raise(ValueError(\"x and y must have the same shape\"))\n", - " return _label_overlap(x, y)\n", - "\n", - "@jit(nopython=True)\n", - "def _label_overlap(x, y):\n", - " x = x.ravel()\n", - " y = y.ravel()\n", - " overlap = np.zeros((1+x.max(),1+y.max()), dtype=np.uint)\n", - " for i in range(len(x)):\n", - " overlap[x[i],y[i]] += 1\n", - " return overlap\n", - "\n", - "\n", - "def intersection_over_union(overlap):\n", - " _check_label_array(overlap,'overlap')\n", - " if np.sum(overlap) == 0:\n", - " return overlap\n", - " n_pixels_pred = np.sum(overlap, axis=0, keepdims=True)\n", - " n_pixels_true = np.sum(overlap, axis=1, keepdims=True)\n", - " return overlap / (n_pixels_pred + n_pixels_true - overlap)\n", - "\n", - "matching_criteria['iou'] = intersection_over_union\n", - "\n", - "\n", - "def intersection_over_true(overlap):\n", - " _check_label_array(overlap,'overlap')\n", - " if np.sum(overlap) == 0:\n", - " return overlap\n", - " n_pixels_true = np.sum(overlap, axis=1, keepdims=True)\n", - " return overlap / n_pixels_true\n", - "\n", - "matching_criteria['iot'] = intersection_over_true\n", - "\n", - "\n", - "def intersection_over_pred(overlap):\n", - " _check_label_array(overlap,'overlap')\n", - " if np.sum(overlap) == 0:\n", - " return overlap\n", - " n_pixels_pred = np.sum(overlap, axis=0, keepdims=True)\n", - " return overlap / n_pixels_pred\n", - "\n", - "matching_criteria['iop'] = intersection_over_pred\n", - "\n", - "\n", - "def precision(tp,fp,fn):\n", - " return tp/(tp+fp) if tp > 0 else 0\n", - "def recall(tp,fp,fn):\n", - " return tp/(tp+fn) if tp > 0 else 0\n", - "def accuracy(tp,fp,fn):\n", - " # also known as \"average precision\" (?)\n", - " # -> https://www.kaggle.com/c/data-science-bowl-2018#evaluation\n", - " return tp/(tp+fp+fn) if tp > 0 else 0\n", - "def f1(tp,fp,fn):\n", - " # also known as \"dice coefficient\"\n", - " return (2*tp)/(2*tp+fp+fn) if tp > 0 else 0\n", - "\n", - "\n", - "def _safe_divide(x,y):\n", - " return x/y if y>0 else 0.0\n", - "\n", - "def matching(y_true, y_pred, thresh=0.5, criterion='iou', report_matches=False):\n", - " \"\"\"Calculate detection/instance segmentation metrics between ground truth and predicted label images.\n", - " Currently, the following metrics are implemented:\n", - " 'fp', 'tp', 'fn', 'precision', 'recall', 'accuracy', 'f1', 'criterion', 'thresh', 'n_true', 'n_pred', 'mean_true_score', 'mean_matched_score', 'panoptic_quality'\n", - " Corresponding objects of y_true and y_pred are counted as true positives (tp), false positives (fp), and false negatives (fn)\n", - " whether their intersection over union (IoU) >= thresh (for criterion='iou', which can be changed)\n", - " * mean_matched_score is the mean IoUs of matched true positives\n", - " * mean_true_score is the mean IoUs of matched true positives but normalized by the total number of GT objects\n", - " * panoptic_quality defined as in Eq. 1 of Kirillov et al. \"Panoptic Segmentation\", CVPR 2019\n", - " Parameters\n", - " ----------\n", - " y_true: ndarray\n", - " ground truth label image (integer valued)\n", - " predicted label image (integer valued)\n", - " thresh: float\n", - " threshold for matching criterion (default 0.5)\n", - " criterion: string\n", - " matching criterion (default IoU)\n", - " report_matches: bool\n", - " if True, additionally calculate matched_pairs and matched_scores (note, that this returns even gt-pred pairs whose scores are below 'thresh')\n", - " Returns\n", - " -------\n", - " Matching object with different metrics as attributes\n", - " Examples\n", - " --------\n", - " >>> y_true = np.zeros((100,100), np.uint16)\n", - " >>> y_true[10:20,10:20] = 1\n", - " >>> y_pred = np.roll(y_true,5,axis = 0)\n", - " >>> stats = matching(y_true, y_pred)\n", - " >>> print(stats)\n", - " Matching(criterion='iou', thresh=0.5, fp=1, tp=0, fn=1, precision=0, recall=0, accuracy=0, f1=0, n_true=1, n_pred=1, mean_true_score=0.0, mean_matched_score=0.0, panoptic_quality=0.0)\n", - " \"\"\"\n", - " _check_label_array(y_true,'y_true')\n", - " _check_label_array(y_pred,'y_pred')\n", - " y_true.shape == y_pred.shape or _raise(ValueError(\"y_true ({y_true.shape}) and y_pred ({y_pred.shape}) have different shapes\".format(y_true=y_true, y_pred=y_pred)))\n", - " criterion in matching_criteria or _raise(ValueError(\"Matching criterion '%s' not supported.\" % criterion))\n", - " if thresh is None: thresh = 0\n", - " thresh = float(thresh) if np.isscalar(thresh) else map(float,thresh)\n", - "\n", - " y_true, _, map_rev_true = relabel_sequential(y_true)\n", - " y_pred, _, map_rev_pred = relabel_sequential(y_pred)\n", - "\n", - " overlap = label_overlap(y_true, y_pred, check=False)\n", - " scores = matching_criteria[criterion](overlap)\n", - " assert 0 <= np.min(scores) <= np.max(scores) <= 1\n", - "\n", - " # ignoring background\n", - " scores = scores[1:,1:]\n", - " n_true, n_pred = scores.shape\n", - " n_matched = min(n_true, n_pred)\n", - "\n", - " def _single(thr):\n", - " not_trivial = n_matched > 0 and np.any(scores >= thr)\n", - " if not_trivial:\n", - " # compute optimal matching with scores as tie-breaker\n", - " costs = -(scores >= thr).astype(float) - scores / (2*n_matched)\n", - " true_ind, pred_ind = linear_sum_assignment(costs)\n", - " assert n_matched == len(true_ind) == len(pred_ind)\n", - " match_ok = scores[true_ind,pred_ind] >= thr\n", - " tp = np.count_nonzero(match_ok)\n", - " else:\n", - " tp = 0\n", - " fp = n_pred - tp\n", - " fn = n_true - tp\n", - " # assert tp+fp == n_pred\n", - " # assert tp+fn == n_true\n", - "\n", - " # the score sum over all matched objects (tp)\n", - " sum_matched_score = np.sum(scores[true_ind,pred_ind][match_ok]) if not_trivial else 0.0\n", - "\n", - " # the score average over all matched objects (tp)\n", - " mean_matched_score = _safe_divide(sum_matched_score, tp)\n", - " # the score average over all gt/true objects\n", - " mean_true_score = _safe_divide(sum_matched_score, n_true)\n", - " panoptic_quality = _safe_divide(sum_matched_score, tp+fp/2+fn/2)\n", - "\n", - " stats_dict = dict (\n", - " criterion = criterion,\n", - " thresh = thr,\n", - " fp = fp,\n", - " tp = tp,\n", - " fn = fn,\n", - " precision = precision(tp,fp,fn),\n", - " recall = recall(tp,fp,fn),\n", - " accuracy = accuracy(tp,fp,fn),\n", - " f1 = f1(tp,fp,fn),\n", - " n_true = n_true,\n", - " n_pred = n_pred,\n", - " mean_true_score = mean_true_score,\n", - " mean_matched_score = mean_matched_score,\n", - " panoptic_quality = panoptic_quality,\n", - " )\n", - " if bool(report_matches):\n", - " if not_trivial:\n", - " stats_dict.update (\n", - " # int() to be json serializable\n", - " matched_pairs = tuple((int(map_rev_true[i]),int(map_rev_pred[j])) for i,j in zip(1+true_ind,1+pred_ind)),\n", - " matched_scores = tuple(scores[true_ind,pred_ind]),\n", - " matched_tps = tuple(map(int,np.flatnonzero(match_ok))),\n", - " )\n", - " else:\n", - " stats_dict.update (\n", - " matched_pairs = (),\n", - " matched_scores = (),\n", - " matched_tps = (),\n", - " )\n", - " return namedtuple('Matching',stats_dict.keys())(*stats_dict.values())\n", - "\n", - " return _single(thresh) if np.isscalar(thresh) else tuple(map(_single,thresh))\n", - "\n", - "\n", - "\n", - "def matching_dataset(y_true, y_pred, thresh=0.5, criterion='iou', by_image=False, show_progress=True, parallel=False):\n", - " \"\"\"matching metrics for list of images, see `stardist.matching.matching`\n", - " \"\"\"\n", - " len(y_true) == len(y_pred) or _raise(ValueError(\"y_true and y_pred must have the same length.\"))\n", - " return matching_dataset_lazy (\n", - " tuple(zip(y_true,y_pred)), thresh=thresh, criterion=criterion, by_image=by_image, show_progress=show_progress, parallel=parallel,\n", - " )\n", - "\n", - "\n", - "\n", - "def matching_dataset_lazy(y_gen, thresh=0.5, criterion='iou', by_image=False, show_progress=True, parallel=False):\n", - "\n", - " expected_keys = set(('fp', 'tp', 'fn', 'precision', 'recall', 'accuracy', 'f1', 'criterion', 'thresh', 'n_true', 'n_pred', 'mean_true_score', 'mean_matched_score', 'panoptic_quality'))\n", - "\n", - " single_thresh = False\n", - " if np.isscalar(thresh):\n", - " single_thresh = True\n", - " thresh = (thresh,)\n", - "\n", - " tqdm_kwargs = {}\n", - " tqdm_kwargs['disable'] = not bool(show_progress)\n", - " if int(show_progress) > 1:\n", - " tqdm_kwargs['total'] = int(show_progress)\n", - "\n", - " # compute matching stats for every pair of label images\n", - " if parallel:\n", - " from concurrent.futures import ThreadPoolExecutor\n", - " fn = lambda pair: matching(*pair, thresh=thresh, criterion=criterion, report_matches=False)\n", - " with ThreadPoolExecutor() as pool:\n", - " stats_all = tuple(pool.map(fn, tqdm(y_gen,**tqdm_kwargs)))\n", - " else:\n", - " stats_all = tuple (\n", - " matching(y_t, y_p, thresh=thresh, criterion=criterion, report_matches=False)\n", - " for y_t,y_p in tqdm(y_gen,**tqdm_kwargs)\n", - " )\n", - "\n", - " # accumulate results over all images for each threshold separately\n", - " n_images, n_threshs = len(stats_all), len(thresh)\n", - " accumulate = [{} for _ in range(n_threshs)]\n", - " for stats in stats_all:\n", - " for i,s in enumerate(stats):\n", - " acc = accumulate[i]\n", - " for k,v in s._asdict().items():\n", - " if k == 'mean_true_score' and not bool(by_image):\n", - " # convert mean_true_score to \"sum_matched_score\"\n", - " acc[k] = acc.setdefault(k,0) + v * s.n_true\n", - " else:\n", - " try:\n", - " acc[k] = acc.setdefault(k,0) + v\n", - " except TypeError:\n", - " pass\n", - "\n", - " # normalize/compute 'precision', 'recall', 'accuracy', 'f1'\n", - " for thr,acc in zip(thresh,accumulate):\n", - " set(acc.keys()) == expected_keys or _raise(ValueError(\"unexpected keys\"))\n", - " acc['criterion'] = criterion\n", - " acc['thresh'] = thr\n", - " acc['by_image'] = bool(by_image)\n", - " if bool(by_image):\n", - " for k in ('precision', 'recall', 'accuracy', 'f1', 'mean_true_score', 'mean_matched_score', 'panoptic_quality'):\n", - " acc[k] /= n_images\n", - " else:\n", - " tp, fp, fn, n_true = acc['tp'], acc['fp'], acc['fn'], acc['n_true']\n", - " sum_matched_score = acc['mean_true_score']\n", - "\n", - " mean_matched_score = _safe_divide(sum_matched_score, tp)\n", - " mean_true_score = _safe_divide(sum_matched_score, n_true)\n", - " panoptic_quality = _safe_divide(sum_matched_score, tp+fp/2+fn/2)\n", - "\n", - " acc.update(\n", - " precision = precision(tp,fp,fn),\n", - " recall = recall(tp,fp,fn),\n", - " accuracy = accuracy(tp,fp,fn),\n", - " f1 = f1(tp,fp,fn),\n", - " mean_true_score = mean_true_score,\n", - " mean_matched_score = mean_matched_score,\n", - " panoptic_quality = panoptic_quality,\n", - " )\n", - "\n", - " accumulate = tuple(namedtuple('DatasetMatching',acc.keys())(*acc.values()) for acc in accumulate)\n", - " return accumulate[0] if single_thresh else accumulate\n", - "\n", - "\n", - "\n", - "# copied from scikit-image master for now (remove when part of a release)\n", - "def relabel_sequential(label_field, offset=1):\n", - " \"\"\"Relabel arbitrary labels to {`offset`, ... `offset` + number_of_labels}.\n", - " This function also returns the forward map (mapping the original labels to\n", - " the reduced labels) and the inverse map (mapping the reduced labels back\n", - " to the original ones).\n", - " Parameters\n", - " ----------\n", - " label_field : numpy array of int, arbitrary shape\n", - " An array of labels, which must be non-negative integers.\n", - " offset : int, optional\n", - " The return labels will start at `offset`, which should be\n", - " strictly positive.\n", - " Returns\n", - " -------\n", - " relabeled : numpy array of int, same shape as `label_field`\n", - " The input label field with labels mapped to\n", - " {offset, ..., number_of_labels + offset - 1}.\n", - " The data type will be the same as `label_field`, except when\n", - " offset + number_of_labels causes overflow of the current data type.\n", - " forward_map : numpy array of int, shape ``(label_field.max() + 1,)``\n", - " The map from the original label space to the returned label\n", - " space. Can be used to re-apply the same mapping. See examples\n", - " for usage. The data type will be the same as `relabeled`.\n", - " inverse_map : 1D numpy array of int, of length offset + number of labels\n", - " The map from the new label space to the original space. This\n", - " can be used to reconstruct the original label field from the\n", - " relabeled one. The data type will be the same as `relabeled`.\n", - " Notes\n", - " -----\n", - " The label 0 is assumed to denote the background and is never remapped.\n", - " The forward map can be extremely big for some inputs, since its\n", - " length is given by the maximum of the label field. However, in most\n", - " situations, ``label_field.max()`` is much smaller than\n", - " ``label_field.size``, and in these cases the forward map is\n", - " guaranteed to be smaller than either the input or output images.\n", - " Examples\n", - " --------\n", - " >>> from skimage.segmentation import relabel_sequential\n", - " >>> label_field = np.array([1, 1, 5, 5, 8, 99, 42])\n", - " >>> relab, fw, inv = relabel_sequential(label_field)\n", - " >>> relab\n", - " array([1, 1, 2, 2, 3, 5, 4])\n", - " >>> fw\n", - " array([0, 1, 0, 0, 0, 2, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", - " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0,\n", - " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", - " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", - " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5])\n", - " >>> inv\n", - " array([ 0, 1, 5, 8, 42, 99])\n", - " >>> (fw[label_field] == relab).all()\n", - " True\n", - " >>> (inv[relab] == label_field).all()\n", - " True\n", - " >>> relab, fw, inv = relabel_sequential(label_field, offset=5)\n", - " >>> relab\n", - " array([5, 5, 6, 6, 7, 9, 8])\n", - " \"\"\"\n", - " offset = int(offset)\n", - " if offset <= 0:\n", - " raise ValueError(\"Offset must be strictly positive.\")\n", - " if np.min(label_field) < 0:\n", - " raise ValueError(\"Cannot relabel array that contains negative values.\")\n", - " max_label = int(label_field.max()) # Ensure max_label is an integer\n", - " if not np.issubdtype(label_field.dtype, np.integer):\n", - " new_type = np.min_scalar_type(max_label)\n", - " label_field = label_field.astype(new_type)\n", - " labels = np.unique(label_field)\n", - " labels0 = labels[labels != 0]\n", - " new_max_label = offset - 1 + len(labels0)\n", - " new_labels0 = np.arange(offset, new_max_label + 1)\n", - " output_type = label_field.dtype\n", - " required_type = np.min_scalar_type(new_max_label)\n", - " if np.dtype(required_type).itemsize > np.dtype(label_field.dtype).itemsize:\n", - " output_type = required_type\n", - " forward_map = np.zeros(max_label + 1, dtype=output_type)\n", - " forward_map[labels0] = new_labels0\n", - " inverse_map = np.zeros(new_max_label + 1, dtype=output_type)\n", - " inverse_map[offset:] = labels0\n", - " relabeled = forward_map[label_field]\n", - " return relabeled, forward_map, inverse_map\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Tbv6DpxZjVN3" - }, - "source": [ - "## **5.1. Inspection of the loss function**\n", - "---\n", - "\n", - "First, it is good practice to evaluate the training progress by comparing the training loss with the validation loss. The latter is a metric which shows how well the network performs on a subset of unseen data which is set aside from the training dataset. For more information on this, see for example [this review](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6381354/) by Nichols *et al.*\n", - "\n", - "**Training loss** describes an error value after each epoch for the difference between the model's prediction and its ground-truth target.\n", - "\n", - "**Validation loss** describes the same error value between the model's prediction on a validation image and compared to it's target.\n", - "\n", - "During training both values should decrease before reaching a minimal value which does not decrease further even after more training. Comparing the development of the validation loss with the training loss can give insights into the model's performance.\n", - "\n", - "Decreasing **Training loss** and **Validation loss** indicates that training is still necessary and increasing the `number_of_epochs` is recommended. Note that the curves can look flat towards the right side, just because of the y-axis scaling. The network has reached convergence once the curves flatten out. After this point no further training is required. If the **Validation loss** suddenly increases again an the **Training loss** simultaneously goes towards zero, it means that the network is overfitting to the training data. In other words the network is remembering the exact patterns from the training data and no longer generalizes well to unseen data. In this case the training dataset has to be increased.\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "id": "jtSv-B0AjX8j" - }, - "outputs": [], - "source": [ - "#@markdown ###Not implemented yet" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s2VXDuiOF7r4" - }, - "source": [ - "## **5.2. Error mapping and quality metrics estimation**\n", - "---\n", - "This section will calculate the Intersection over Union score for all the images provided in the Source_QC_folder and Target_QC_folder ! The result for one of the image will also be displayed.\n", - "\n", - "The **Intersection over Union** (IuO) metric is a method that can be used to quantify the percent overlap between the target mask and your prediction output. **Therefore, the closer to 1, the better the performance.** This metric can be used to assess the quality of your model to accurately predict nuclei.\n", - "\n", - "Here, the IuO is both calculated over the whole image and on a per-object basis. The value displayed below is the IuO value calculated over the entire image. The IuO value calculated on a per-object basis is used to calculate the other metrics displayed.\n", - "\n", - "“n_true” refers to the number of objects present in the ground truth image. “n_pred” refers to the number of objects present in the predicted image.\n", - "\n", - "When a segmented object has an IuO value above 0.5 (compared to the corresponding ground truth), it is then considered a true positive. The number of “**true positives**” is available in the table below. The number of “false positive” is then defined as “**false positive**” = “n_pred” - “true positive”. The number of “false negative” is defined as “false negative” = “n_true” - “true positive”.\n", - "\n", - "The mean_matched_score is the mean IoUs of matched true positives. The mean_true_score is the mean IoUs of matched true positives but normalized by the total number of ground truth objects. The panoptic_quality is calculated as described by [Kirillov et al. 2019](https://arxiv.org/abs/1801.00868).\n", - "\n", - "For more information about the other metric displayed, please consult the SI of the paper describing ZeroCostDL4Mic.\n", - "\n", - " The results can be found in the \"*Quality Control*\" folder which is located inside your \"model_folder\".\n", - "\n", - "**`Channel_to_segment`:** Choose the channel to segment. If using single-channel grayscale images, choose \"Grayscale\".\n", - "\n", - "**`Nuclear_channel`:** If you are using a model that segment the \"cytoplasm\", you can use a nuclear channel to aid the segmentation.\n", - "\n", - "**`Object_diameter`:** Indicate the diameter of the objects (cells or Nuclei) you want to segment (in pixel). If you input \"0\", this parameter will be estimated automatically for each of your images.\n", - "\n", - "**`Flow_threshold`:** This parameter controls the maximum allowed error of the flows for each mask. Increase this threshold if cellpose is not returning as many masks as you'd expect. Similarly, decrease this threshold if cellpose is returning too many ill-shaped masks. Set to 0.0 to turn off so no cells are discarded. **Default value: 0.4**\n", - "\n", - "**`mask_threshold`:** The pixels greater than the mask_threshold are used to run dynamics and determine masks. Decrease this threshold if cellpose is not returning as many masks as you'd expect. Similarly, increase this threshold if cellpose is returning too many masks, particularly from dim areas. **Default value: 0.0**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "id": "BUrTuonhEH5J" - }, - "outputs": [], - "source": [ - "#@markdown ##Choose the folders that contain your Quality Control dataset\n", - "\n", - "Source_QC_folder = \"\" #@param{type:\"string\"}\n", - "Target_QC_folder = \"\" #@param{type:\"string\"}\n", - "\n", - "Channel_to_segment= \"Red\" #@param [\"Grayscale\", \"Blue\", \"Green\", \"Red\"]\n", - "\n", - "# @markdown ###If you chose the model \"cytoplasm\" indicate if you also have a nuclear channel that can be used to aid the segmentation.\n", - "\n", - "Nuclear_channel= \"None\" #@param [\"None\", \"Blue\", \"Green\", \"Red\"]\n", - "\n", - "#@markdown ### Segmentation parameters:\n", - "Object_diameter = 0#@param {type:\"number\"}\n", - "\n", - "Flow_threshold = 0.4 #@param {type:\"slider\", min:0.0, max:3, step:0.1}\n", - "\n", - "mask_threshold=0 #@param {type:\"slider\", min:-6, max:6, step:1}\n", - "\n", - "if Object_diameter is 0:\n", - " Object_diameter = None\n", - " print(\"The cell size will be estimated automatically for each image\")\n", - "\n", - "\n", - "# Find the number of channel in the input image\n", - "\n", - "random_choice = random.choice(os.listdir(Source_QC_folder))\n", - "x = io.imread(Source_QC_folder+\"/\"+random_choice)\n", - "n_channel = 1 if x.ndim == 2 else x.shape[-1]\n", - "\n", - "if Channel_to_segment == \"Grayscale\":\n", - " segment_channel = 0\n", - "\n", - " if not n_channel == 1:\n", - " print(bcolors.WARNING +\"!! WARNING: your image has more than one channel, choose which channel you want to use for QC !!\")\n", - "\n", - "if Channel_to_segment == \"Blue\":\n", - " segment_channel = 3\n", - "\n", - "if Channel_to_segment == \"Green\":\n", - " segment_channel = 2\n", - "\n", - "if Channel_to_segment == \"Red\":\n", - " segment_channel = 1\n", - "\n", - "if Nuclear_channel == \"Blue\":\n", - " nuclear_channel = 3\n", - "\n", - "if Nuclear_channel == \"Green\":\n", - " nuclear_channel = 2\n", - "\n", - "if Nuclear_channel == \"Red\":\n", - " nuclear_channel = 1\n", - "\n", - "if Nuclear_channel == \"None\":\n", - " nuclear_channel = 0\n", - "\n", - "if QC_model == \"Cytoplasm\":\n", - " channels=[segment_channel,nuclear_channel]\n", - "\n", - "if QC_model == \"Cytoplasm2_Omnipose\":\n", - " channels=[segment_channel,nuclear_channel]\n", - "\n", - "if QC_model == \"Bacteria_Omnipose\":\n", - " channels=[segment_channel,nuclear_channel]\n", - "\n", - "if QC_model == \"Cytoplasm2\":\n", - " channels=[segment_channel,nuclear_channel]\n", - "\n", - "if QC_model == \"Nuclei\":\n", - " channels=[segment_channel,0]\n", - "\n", - "if QC_model == \"Own_model\":\n", - " channels=[segment_channel,nuclear_channel]\n", - "\n", - "#Create a quality control Folder and check if the folder already exist\n", - "if os.path.exists(QC_model_folder+\"/Quality Control\") == False:\n", - " os.makedirs(QC_model_folder+\"/Quality Control\")\n", - "\n", - "if os.path.exists(QC_model_folder+\"/Quality Control/Prediction\"):\n", - " shutil.rmtree(QC_model_folder+\"/Quality Control/Prediction\")\n", - "os.makedirs(QC_model_folder+\"/Quality Control/Prediction\")\n", - "\n", - "\n", - "# Here we need to make predictions\n", - "\n", - "for name in os.listdir(Source_QC_folder):\n", - "\n", - " print(\"Performing prediction on: \"+name)\n", - " image = io.imread(Source_QC_folder+\"/\"+name)\n", - "\n", - " short_name = os.path.splitext(name)\n", - "\n", - " if QC_model == \"Own_model\" or QC_model == \"LiveCell\" or QC_model == \"TissueNet\":\n", - " masks, flows, styles = model.eval(image, diameter=Object_diameter, flow_threshold=Flow_threshold,cellprob_threshold=mask_threshold, channels=channels)\n", - " else:\n", - " masks, flows, styles, diams = model.eval(image, diameter=Object_diameter, flow_threshold=Flow_threshold,cellprob_threshold=mask_threshold, channels=channels)\n", - "\n", - " os.chdir(QC_model_folder+\"/Quality Control/Prediction\")\n", - " imsave(str(short_name[0])+\".tif\", masks)\n", - "\n", - "# Here we start testing the differences between GT and predicted masks\n", - "\n", - "with open(QC_model_folder+\"/Quality Control/Quality_Control for \"+QC_model_name+\".csv\", \"w\", newline='') as file:\n", - " writer = csv.writer(file, delimiter=\",\")\n", - " writer.writerow([\"image\",\"Prediction v. GT Intersection over Union\", \"false positive\", \"true positive\", \"false negative\", \"precision\", \"recall\", \"accuracy\", \"f1 score\", \"n_true\", \"n_pred\", \"mean_true_score\", \"mean_matched_score\", \"panoptic_quality\"])\n", - "\n", - "# define the images\n", - "\n", - " for n in os.listdir(Source_QC_folder):\n", - "\n", - " if not os.path.isdir(os.path.join(Source_QC_folder,n)):\n", - " print('Running QC on: '+n)\n", - " test_input = io.imread(os.path.join(Source_QC_folder,n))\n", - " test_prediction = io.imread(os.path.join(QC_model_folder+\"/Quality Control/Prediction\",n))\n", - " test_ground_truth_image = io.imread(os.path.join(Target_QC_folder, n))\n", - "\n", - " # Calculate the matching (with IoU threshold `thresh`) and all metrics\n", - "\n", - " stats = matching(test_ground_truth_image, test_prediction, thresh=0.5)\n", - "\n", - "\n", - " #Convert pixel values to 0 or 255\n", - " test_prediction_0_to_255 = test_prediction\n", - " test_prediction_0_to_255[test_prediction_0_to_255>0] = 255\n", - "\n", - " #Convert pixel values to 0 or 255\n", - " test_ground_truth_0_to_255 = test_ground_truth_image\n", - " test_ground_truth_0_to_255[test_ground_truth_0_to_255>0] = 255\n", - "\n", - "\n", - " # Intersection over Union metric\n", - "\n", - " intersection = np.logical_and(test_ground_truth_0_to_255, test_prediction_0_to_255)\n", - " union = np.logical_or(test_ground_truth_0_to_255, test_prediction_0_to_255)\n", - " iou_score = np.sum(intersection) / np.sum(union)\n", - " writer.writerow([n, str(iou_score), str(stats.fp), str(stats.tp), str(stats.fn), str(stats.precision), str(stats.recall), str(stats.accuracy), str(stats.f1), str(stats.n_true), str(stats.n_pred), str(stats.mean_true_score), str(stats.mean_matched_score), str(stats.panoptic_quality)])\n", - "\n", - "from tabulate import tabulate\n", - "\n", - "df = pd.read_csv (QC_model_folder+\"/Quality Control/Quality_Control for \"+QC_model_name+\".csv\")\n", - "print(tabulate(df, headers='keys', tablefmt='psql'))\n", - "\n", - "\n", - "from astropy.visualization import simple_norm\n", - "\n", - "# ------------- For display ------------\n", - "print('--------------------------------------------------------------')\n", - "@interact\n", - "def show_QC_results(file = os.listdir(Source_QC_folder)):\n", - "\n", - "\n", - " plt.figure(figsize=(25,5))\n", - " if n_channel > 1:\n", - " source_image = io.imread(os.path.join(Source_QC_folder, file))\n", - " if n_channel == 1:\n", - " source_image = io.imread(os.path.join(Source_QC_folder, file), as_gray = True)\n", - "\n", - " target_image = io.imread(os.path.join(Target_QC_folder, file), as_gray = True)\n", - " prediction = io.imread(QC_model_folder+\"/Quality Control/Prediction/\"+file, as_gray = True)\n", - "\n", - " stats = matching(prediction, target_image, thresh=0.5)\n", - "\n", - " target_image_mask = np.empty_like(target_image)\n", - " target_image_mask[target_image > 0] = 255\n", - " target_image_mask[target_image == 0] = 0\n", - "\n", - " prediction_mask = np.empty_like(prediction)\n", - " prediction_mask[prediction > 0] = 255\n", - " prediction_mask[prediction == 0] = 0\n", - "\n", - " intersection = np.logical_and(target_image_mask, prediction_mask)\n", - " union = np.logical_or(target_image_mask, prediction_mask)\n", - " iou_score = np.sum(intersection) / np.sum(union)\n", - "\n", - " norm = simple_norm(source_image, percent = 99)\n", - "\n", - " #Input\n", - " plt.subplot(1,4,1)\n", - " plt.axis('off')\n", - " if n_channel > 1:\n", - " plt.imshow(source_image)\n", - " if n_channel == 1:\n", - " plt.imshow(source_image, aspect='equal', norm=norm, cmap='magma', interpolation='nearest')\n", - " plt.title('Input')\n", - "\n", - " #Ground-truth\n", - " plt.subplot(1,4,2)\n", - " plt.axis('off')\n", - " plt.imshow(target_image_mask, aspect='equal', cmap='Greens')\n", - " plt.title('Ground Truth')\n", - "\n", - " #Prediction\n", - " plt.subplot(1,4,3)\n", - " plt.axis('off')\n", - " plt.imshow(prediction_mask, aspect='equal', cmap='Purples')\n", - " plt.title('Prediction')\n", - "\n", - " #Overlay\n", - " plt.subplot(1,4,4)\n", - " plt.axis('off')\n", - " plt.imshow(target_image_mask, cmap='Greens')\n", - " plt.imshow(prediction_mask, alpha=0.5, cmap='Purples')\n", - " plt.title('Ground Truth and Prediction, Intersection over Union:'+str(round(iou_score,3 )));\n", - " plt.savefig(QC_model_folder+'/Quality Control/QC_example_data.png',bbox_inches='tight',pad_inches=0)\n", - "\n", - "\n", - "full_QC_model_path = QC_model_folder+'/'\n", - "qc_pdf_export()\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Io62PUMLagFS" - }, - "source": [ - "# **6. Using the trained model**\n", - "\n", - "---\n", - "\n", - "In this section the unseen data is processed using the trained model (in section 4). First, your unseen images are uploaded and prepared for prediction. After that your trained model from section 4 is activated and finally saved into your Google Drive." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "E29LWfWpjkZU" - }, - "source": [ - "\n", - "\n", - "## **6.1 Generate prediction(s) from unseen dataset (2D and 2D + t data)**\n", - "---\n", - "\n", - "The current trained model (from section 4.3) can now be used to process images. If an older model needs to be used, please untick the **Use_the_current_trained_model** box and enter the model's name and path to use. Predicted output images are saved in your **Prediction_folder** folder as restored image stacks (ImageJ-compatible TIFF images).\n", - "\n", - "**`Data_folder`:** This folder should contain the images that you want to predict using the network that you will train.\n", - "\n", - "**`Result_folder`:** This folder will contain the predicted output.\n", - "\n", - "**`Data_type`:** Please indicate if the images you want to predict are single images or stacks\n", - "\n", - "**`model_choice`:** Choose the model to use to make predictions. This model needs to be a Cellpose model. You can also use the pretrained models already available in cellpose:\n", - "\n", - "- The cytoplasm model in cellpose is trained on two-channel images, where the first channel is the channel to segment, and the second channel is an optional nuclear channel.\n", - "\n", - "- The cytoplasm2 model is an updated cytoplasm model trained with user-submitted images.\n", - "\n", - "- The Nuclei model in cellpose is trained on two-channel images, where the first channel is the channel to segment, and the second channel is always set to an array of zeros.\n", - "\n", - "- The 'LiveCell' model was trained on all training images from the [livecell dataset](https://sartorius-research.github.io/LIVECell/). These images only have a cytoplasm channel.\n", - "\n", - "- The 'TissueNet' model was trained on all training images from the [tissuenet dataset](https://datasets.deepcell.org/). These images have a cytoplasm channel and a nuclear channel\n", - "\n", - "- The bacteria Omnipose model is an Omnipose model dedicated to the segmentation of bacteria or other elongated cells.\n", - "\n", - "- The cytoplasm2 Omnipose model is an Omnipose model trained using the same dataset as the cellpose cytoplasm2 model.\n", - "\n", - "**`Channel_to_segment`:** Choose the channel to segment. If using single-channel grayscale images, choose \"Grayscale\".\n", - "\n", - "**`Nuclear_channel`:** If you are using a model that segment the \"cytoplasm\", you can use a nuclear channel to aid the segmentation.\n", - "\n", - "**`Object_diameter`:** Indicate the diameter of the objects (cells or Nuclei) you want to segment (in pixel). If you input \"0\", this parameter will be estimated automatically for each of your images.\n", - "\n", - "**`Flow_threshold`:** This parameter controls the maximum allowed error of the flows for each mask. Increase this threshold if cellpose is not returning as many masks as you'd expect. Similarly, decrease this threshold if cellpose is returning too many ill-shaped masks. **Default value: 0.4**\n", - "\n", - "**`mask_threshold`:** The pixels greater than the mask_threshold are used to run dynamics and determine masks. Decrease this threshold if cellpose is not returning as many masks as you'd expect. Similarly, increase this threshold if cellpose is returning too many masks, particularly from dim areas. **Default value: 0.0**\n", - "\n", - "**IMPORTANT:** One example result will be displayed first so that you can assess the quality of the prediction and change your settings accordingly. Once the most suitable settings have been chosen, press on the yellow button \"process your images\".\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "id": "mfgvhMk2xid9" - }, - "outputs": [], - "source": [ - "\n", - "#@markdown ### Provide the path to your dataset and to the folder where the predictions are saved, then play the cell to predict outputs from your unseen images.\n", - "\n", - "Data_folder = \"\" #@param {type:\"string\"}\n", - "Result_folder = \"\" #@param {type:\"string\"}\n", - "\n", - "#@markdown ###Are your data single images or stacks?\n", - "\n", - "Data_type = \"Single_Images\" #@param [\"Single_Images\", \"Stacks (2D + t)\"]\n", - "\n", - "#@markdown ###What model do you want to use?\n", - "\n", - "model_choice = \"Cytoplasm3\" #@param [\"Cytoplasm\",\"Cytoplasm2\",\"Cytoplasm3\",\"LiveCell\", \"TissueNet\", \"Cytoplasm2_Omnipose\", \"Bacteria_Omnipose\", \"Nuclei\", \"Own_model\"]\n", - "\n", - "#@markdown ####If using your own model, please provide the path to the model (not the folder):\n", - "\n", - "Prediction_model = \"\" #@param {type:\"string\"}\n", - "\n", - "#@markdown ### What channel do you want to segment?\n", - "\n", - "Channel_to_segment= \"Red\" #@param [\"Grayscale\", \"Blue\", \"Green\", \"Red\"]\n", - "\n", - "# @markdown ###If you chose the model \"cytoplasm\" indicate if you also have a nuclear channel that can be used to aid the segmentation.\n", - "\n", - "Nuclear_channel= \"Blue\" #@param [\"None\", \"Blue\", \"Green\", \"Red\"]\n", - "\n", - "#@markdown ### Segmentation parameters:\n", - "Object_diameter = 500#@param {type:\"number\"}\n", - "\n", - "Flow_threshold = 1.5 #@param {type:\"slider\", min:0.0, max:3, step:0.1}\n", - "\n", - "mask_threshold=0 #@param {type:\"slider\", min:-6, max:6, step:1}\n", - "\n", - "# Find the number of channel in the input image\n", - "\n", - "random_choice = random.choice(os.listdir(Data_folder))\n", - "x = io.imread(Data_folder+\"/\"+random_choice)\n", - "n_channel = 1 if x.ndim == 2 else x.shape[-1]\n", - "\n", - "if Channel_to_segment == \"Grayscale\":\n", - " segment_channel = 0\n", - "\n", - " if Data_type == \"Single_Images\":\n", - " if not n_channel == 1:\n", - " print(bcolors.WARNING +\"!! WARNING: your image has more than one channel, choose which channel you want to use for your predictions !!\")\n", - "\n", - "if Channel_to_segment == \"Blue\":\n", - " segment_channel = 3\n", - "\n", - "if Channel_to_segment == \"Green\":\n", - " segment_channel = 2\n", - "\n", - "if Channel_to_segment == \"Red\":\n", - " segment_channel = 1\n", - "\n", - "if Nuclear_channel == \"Blue\":\n", - " nuclear_channel = 3\n", - "\n", - "if Nuclear_channel == \"Green\":\n", - " nuclear_channel = 2\n", - "\n", - "if Nuclear_channel == \"Red\":\n", - " nuclear_channel = 1\n", - "\n", - "if Nuclear_channel == \"None\":\n", - " nuclear_channel = 0\n", - "\n", - "if model_choice == \"Cytoplasm\":\n", - " channels=[segment_channel,nuclear_channel]\n", - " model = models.Cellpose(gpu=True, model_type=\"cyto\")\n", - " print(\"Cytoplasm model enabled\")\n", - "\n", - "if model_choice == \"Cytoplasm3\":\n", - " channels=[segment_channel,nuclear_channel]\n", - " model = models.Cellpose(gpu=True, model_type=\"cyto3\")\n", - " print(\"Cytoplasm 3 model enabled\")\n", - "\n", - "if model_choice == \"Cytoplasm2\":\n", - " channels=[segment_channel,nuclear_channel]\n", - " model = models.Cellpose(gpu=True, model_type=\"cyto2\")\n", - " print(\"Cytoplasm2 model enabled\")\n", - "\n", - "if model_choice == \"Cytoplasm2_Omnipose\":\n", - " channels=[segment_channel,nuclear_channel]\n", - " model = models.Cellpose(gpu=True, model_type=\"cyto2_omni\")\n", - " print(\"Cytoplasm2_Omnipose model enabled\")\n", - "\n", - "if model_choice == \"Nuclei\":\n", - " channels=[segment_channel,0]\n", - " model = models.Cellpose(gpu=True, model_type=\"nuclei\")\n", - " print(\"Nuclei model enabled\")\n", - "\n", - "if model_choice == \"LiveCell\":\n", - " channels=[segment_channel,0]\n", - " model = models.CellposeModel(gpu=True, model_type=\"livecell\")\n", - " print(\"LiveCell model enabled\")\n", - "\n", - "if model_choice == \"Bacteria_Omnipose\":\n", - " channels=[segment_channel,nuclear_channel]\n", - " model = models.Cellpose(gpu=True, model_type=\"bact_omni\")\n", - " Object_diameter = 0\n", - " print(\"Bacteria_omnipose model enabled\")\n", - "\n", - "if model_choice == \"TissueNet\":\n", - " channels=[segment_channel,nuclear_channel]\n", - " model = models.CellposeModel(gpu=True, model_type='tissuenet')\n", - " print(\"TissueNet model enabled\")\n", - "\n", - "if model_choice == \"Own_model\":\n", - " channels=[segment_channel,nuclear_channel]\n", - " model = models.CellposeModel(gpu=True, pretrained_model=Prediction_model)\n", - "\n", - " print(\"Own model enabled\")\n", - "\n", - "if Object_diameter is 0:\n", - " Object_diameter = None\n", - " print(\"The cell size will be estimated automatically for each image\")\n", - "\n", - "if Data_type == \"Single_Images\" :\n", - "\n", - " print('--------------------------------------------------------------')\n", - " @interact\n", - " def preview_results(file = os.listdir(Data_folder)):\n", - " source_image = io.imread(os.path.join(Data_folder, file))\n", - "\n", - " if model_choice == \"Own_model\" or model_choice == \"LiveCell\" or model_choice == \"TissueNet\":\n", - " masks, flows, styles = model.eval(source_image, diameter=Object_diameter, flow_threshold=Flow_threshold,cellprob_threshold=mask_threshold, channels=channels)\n", - "\n", - " else:\n", - " masks, flows, styles, diams = model.eval(source_image, diameter=Object_diameter, flow_threshold=Flow_threshold,cellprob_threshold=mask_threshold, channels=channels)\n", - "\n", - " flowi = flows[0]\n", - " fig = plt.figure(figsize=(20,10))\n", - " plot.show_segmentation(fig, source_image, masks, flowi, channels=channels)\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "\n", - " def batch_process():\n", - " print(\"Your images are now beeing processed\")\n", - "\n", - " for name in os.listdir(Data_folder):\n", - " print(\"Performing prediction on: \"+name)\n", - " image = io.imread(Data_folder+\"/\"+name)\n", - " short_name = os.path.splitext(name)\n", - "\n", - " if model_choice == \"Own_model\" or model_choice == \"LiveCell\" or model_choice == \"TissueNet\":\n", - " masks, flows, styles = model.eval(image, diameter=Object_diameter, flow_threshold=Flow_threshold,cellprob_threshold=mask_threshold, channels=channels)\n", - " else:\n", - " masks, flows, styles, diams = model.eval(image, diameter=Object_diameter, flow_threshold=Flow_threshold,cellprob_threshold=mask_threshold, channels=channels)\n", - "\n", - " os.chdir(Result_folder)\n", - " imsave(str(short_name[0])+\"_mask.tif\", masks)\n", - "\n", - " im = interact_manual(batch_process)\n", - " im.widget.children[0].description = 'Process your images'\n", - " im.widget.children[0].style.button_color = 'yellow'\n", - " display(im)\n", - "\n", - "if Data_type == \"Stacks (2D + t)\" :\n", - " print(\"Stacks (2D + t) are now beeing predicted\")\n", - "\n", - " print('--------------------------------------------------------------')\n", - " @interact\n", - " def preview_results_stacks(file = os.listdir(Data_folder)):\n", - " timelapse = imread(Data_folder+\"/\"+file)\n", - "\n", - " if model_choice == \"Own_model\" or model_choice == \"LiveCell\" or model_choice == \"TissueNet\":\n", - " masks, flows, styles = model.eval(timelapse[0], diameter=Object_diameter, flow_threshold=Flow_threshold,cellprob_threshold=mask_threshold, channels=channels)\n", - " else:\n", - " masks, flows, styles, diams = model.eval(timelapse[0], diameter=Object_diameter, flow_threshold=Flow_threshold,cellprob_threshold=mask_threshold, channels=channels)\n", - "\n", - " flowi = flows[0]\n", - " fig = plt.figure(figsize=(20,10))\n", - " plot.show_segmentation(fig, timelapse[0], masks, flowi, channels=channels)\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - " def batch_process_stack():\n", - " print(\"Your images are now beeing processed\")\n", - " for image in os.listdir(Data_folder):\n", - " print(\"Performing prediction on: \"+image)\n", - " timelapse = imread(Data_folder+\"/\"+image)\n", - " short_name = os.path.splitext(image)\n", - " n_timepoint = timelapse.shape[0]\n", - " prediction_stack = np.zeros((n_timepoint, timelapse.shape[1], timelapse.shape[2]))\n", - "\n", - " for t in range(n_timepoint):\n", - " print(\"Frame number: \"+str(t))\n", - " img_t = timelapse[t]\n", - "\n", - " if model_choice == \"Own_model\" or model_choice == \"LiveCell\" or model_choice == \"TissueNet\":\n", - " masks, flows, styles = model.eval(img_t, diameter=Object_diameter, flow_threshold=Flow_threshold,cellprob_threshold=mask_threshold, channels=channels)\n", - " else:\n", - " masks, flows, styles, diams = model.eval(img_t, diameter=Object_diameter, flow_threshold=Flow_threshold,cellprob_threshold=mask_threshold, channels=channels)\n", - "\n", - "\n", - " prediction_stack[t] = masks\n", - "\n", - " prediction_stack = img_as_float32(prediction_stack, force_copy=False)\n", - " os.chdir(Result_folder)\n", - " imsave(str(short_name[0])+\".tif\", prediction_stack, compression ='zlib')\n", - " del prediction_stack\n", - "\n", - " im = interact_manual(batch_process_stack)\n", - " im.widget.children[0].description = 'Process your images'\n", - " im.widget.children[0].style.button_color = 'yellow'\n", - " display(im)\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "rk7GNKr5tYBY" - }, - "source": [ - "\n", - "\n", - "## **6.2 Generate prediction(s) from unseen dataset (3D stacks)**\n", - "---\n", - "\n", - "The current trained model (from section 4.3) can now be used to process images. If an older model needs to be used, please untick the **Use_the_current_trained_model** box and enter the model's name and path to use. Predicted output images are saved in your **Prediction_folder** folder as restored image stacks (ImageJ-compatible TIFF images).\n", - "\n", - "**`Data_folder`:** This folder should contain the images that you want to predict using the network that you will train.\n", - "\n", - "**`Result_folder`:** This folder will contain the predicted output.\n", - "\n", - "**`model_choice`:** Choose the model to use to make predictions. This model needs to be a Cellpose model. You can also use the pretrained models already available in cellpose:\n", - "\n", - "- The cytoplasm model in cellpose is trained on two-channel images, where the first channel is the channel to segment, and the second channel is an optional nuclear channel.\n", - "- The cytoplasm2 model is an updated cytoplasm model trained with user-submitted images.\n", - "\n", - "- The nuclear model in cellpose is trained on two-channel images, where the first channel is the channel to segment, and the second channel is always set to an array of zeros.\n", - "\n", - "\n", - "**`Channel_to_segment`:** Choose the channel to segment. If using single-channel grayscale images, choose \"Grayscale\".\n", - "\n", - "**`Nuclear_channel`:** If you are using a model that segment the \"cytoplasm\", you can use a nuclear channel to aid the segmentation.\n", - "\n", - "**`Object_diameter`:** Indicate the diameter of the objects (cells or Nuclei) you want to segment (in pixel). Do not input \"0\" to perform 3D predictions.\n", - "\n", - "**`Anisotropy`:** Optional rescaling factor to use when the images to segment are not isotropic (e.g. set to 2.0 if Z is sampled half as dense as X or Y). **Default value: 1**\n", - "\n", - "**`Min_size`:** Minimum number of pixels per mask. Can be turned off using -1. **Default value: 15**\n", - "\n", - "**`mask_threshold`:** The pixels greater than the mask_threshold are used to run dynamics and determine masks. Decrease this threshold if cellpose is not returning as many masks as you'd expect. Similarly, increase this threshold if cellpose is returning too many masks, particularly from dim areas. **Default value: 0.0**\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "id": "Qf2Mn-2Yo7tI" - }, - "outputs": [], - "source": [ - "from cellpose import utils\n", - "from numba.experimental import jitclass\n", - "from numba import jit, float64\n", - "\n", - "\n", - "#@markdown ### Provide the path to your dataset and to the folder where the predictions are saved, then play the cell to predict outputs from your unseen images.\n", - "\n", - "Data_folder = \"\" #@param {type:\"string\"}\n", - "Result_folder = \"\" #@param {type:\"string\"}\n", - "\n", - "#@markdown ###What model do you want to use?\n", - "model_choice = \"Cytoplasm\" #@param [\"Cytoplasm\",\"Cytoplasm2\", \"Nuclei\", \"Own_model\"]\n", - "\n", - "\n", - "#@markdown ####If using your own model, please provide the path to the model (not the folder):\n", - "\n", - "Prediction_model = \"\" #@param {type:\"string\"}\n", - "\n", - "#@markdown ### What channel do you want to segment?\n", - "\n", - "Channel_to_segment= \"Grayscale\" #@param [\"Grayscale\", \"Blue\", \"Green\", \"Red\"]\n", - "\n", - "# @markdown ###If you chose the model \"cytoplasm\" indicate if you also have a nuclear channel that can be used to aid the segmentation.\n", - "\n", - "Nuclear_channel= \"None\" #@param [\"None\", \"Blue\", \"Green\", \"Red\"]\n", - "\n", - "#@markdown ###How do you want to generate the 3D labels?\n", - "\n", - "#Choose_3D_mode = \"2D predictions and stitching\" #@param [\"3D predictions\", \"2D predictions and stitching\"]\n", - "\n", - "Choose_3D_mode = \"3D predictions\" #@param [\"3D predictions\"]\n", - "\n", - "#@markdown ### Segmentation parameters:\n", - "Object_diameter = 10#@param {type:\"number\"}\n", - "Anisotropy = 1 #@param {type:\"number\"}\n", - "Min_size = 200#@param {type:\"number\"}\n", - "mask_threshold=0 #@param {type:\"slider\", min:-6, max:6, step:1}\n", - "\n", - "##@markdown ### Additional parameters for 2D predictions and stitching:\n", - "\n", - "#Flow_threshold = 0.5 #@param {type:\"slider\", min:0.1, max:1.1, step:0.1}\n", - "#Stitch_threshold= 0.1#@param {type:\"slider\", min:0, max:1.0, step:0.1}\n", - "\n", - "#failsafes\n", - "if Choose_3D_mode == \"3D predictions\":\n", - " do_3D = True\n", - " Stitch_threshold= 0\n", - "\n", - " if Object_diameter == 0:\n", - " Object_diameter= 40\n", - " print(\"Object_diameter set to 40. If you want to use a different Object_diameter enable it in your segmentation parameters\")\n", - "\n", - "else:\n", - " do_3D = False\n", - " Anisotropy = 0\n", - "\n", - "if Choose_3D_mode == \"2D predictions and stitching\" and Stitch_threshold== 0:\n", - " Stitch_threshold= 0.5\n", - " print(\"Stitch_threshold has now automatically be enabled with an IoU of 0.5. If you want to use a different threshold enable it in your segmentation parameters\")\n", - "\n", - "\n", - "if Channel_to_segment == \"Grayscale\":\n", - " segment_channel = 0\n", - "\n", - "if Channel_to_segment == \"Blue\":\n", - " segment_channel = 3\n", - "\n", - "if Channel_to_segment == \"Green\":\n", - " segment_channel = 2\n", - "\n", - "if Channel_to_segment == \"Red\":\n", - " segment_channel = 1\n", - "\n", - "if Nuclear_channel == \"Blue\":\n", - " nuclear_channel = 3\n", - "\n", - "if Nuclear_channel == \"Green\":\n", - " nuclear_channel = 2\n", - "\n", - "if Nuclear_channel == \"Red\":\n", - " nuclear_channel = 1\n", - "\n", - "if Nuclear_channel == \"None\":\n", - " nuclear_channel = 0\n", - "\n", - "if model_choice == \"Cytoplasm\":\n", - " channels=[segment_channel,nuclear_channel]\n", - " model = models.Cellpose(gpu=True, model_type=\"cyto\")\n", - " print(\"Cytoplasm model enabled\")\n", - "\n", - "if model_choice == \"Cytoplasm2\":\n", - " channels=[segment_channel,nuclear_channel]\n", - " model = models.Cellpose(gpu=True, model_type=\"cyto2\")\n", - " print(\"Cytoplasm2 model enabled\")\n", - "\n", - "if model_choice == \"Nuclei\":\n", - " channels=[segment_channel,0]\n", - " model = models.Cellpose(gpu=True, model_type=\"nuclei\")\n", - " print(\"Nuclei model enabled\")\n", - "\n", - "if model_choice == \"Cytoplasm2_Omnipose\":\n", - " channels=[segment_channel,nuclear_channel]\n", - " model = models.Cellpose(gpu=True, model_type=\"cyto2_omni\")\n", - " print(\"Cytoplasm2_Omnipose model enabled\")\n", - "\n", - "if model_choice == \"Bacteria_Omnipose\":\n", - " channels=[segment_channel,nuclear_channel]\n", - " model = models.Cellpose(gpu=True, model_type=\"bact_omni\")\n", - " print(\"Bacteria_omnipose model enabled\")\n", - "\n", - "if model_choice == \"Own_model\":\n", - " channels=[segment_channel,nuclear_channel]\n", - " model = models.CellposeModel(gpu=True, pretrained_model=Prediction_model, torch=True, diam_mean=30.0, net_avg=True, device=None, residual_on=True, style_on=True, concatenation=False)\n", - "\n", - " print(\"Own model enabled\")\n", - "\n", - "if Object_diameter is 0:\n", - " Object_diameter = None\n", - " print(\"The cell size will be estimated automatically for each image\")\n", - "\n", - "if Choose_3D_mode == \"2D predictions and stitching\":\n", - "\n", - " for image in os.listdir(Data_folder):\n", - " print(\"Performing prediction on: \"+image)\n", - " stack = imread(Data_folder+\"/\"+image)\n", - " short_name = os.path.splitext(image)\n", - " n_plane = stack.shape[0]\n", - " prediction_stack = np.zeros((n_plane, stack.shape[1], stack.shape[2]))\n", - "\n", - " for t in range(n_plane):\n", - " print(\"Z plane number: \"+str(t))\n", - " img_t = stack[t]\n", - "\n", - " if model_choice == \"Own_model\":\n", - " masks, flows, styles = model.eval(img_t, diameter=Object_diameter, flow_threshold=Flow_threshold,cellprob_threshold=mask_threshold, channels=channels, do_3D=do_3D, anisotropy = Anisotropy, stitch_threshold=Stitch_threshold, min_size=Min_size)\n", - " else:\n", - " masks, flows, styles, diams = model.eval(img_t, diameter=Object_diameter, flow_threshold=Flow_threshold,cellprob_threshold=mask_threshold, channels=channels, do_3D=do_3D, anisotropy = Anisotropy, stitch_threshold=Stitch_threshold, min_size=Min_size)\n", - "\n", - " prediction_stack = masks\n", - "\n", - " os.chdir(Result_folder)\n", - " imsave(str(short_name[0])+\".tif\", prediction_stack)\n", - "\n", - "\n", - "if Choose_3D_mode == \"3D predictions\":\n", - "\n", - " for image in os.listdir(Data_folder):\n", - " print(\"Performing prediction on: \"+image)\n", - " stack = imread(Data_folder+\"/\"+image)\n", - " short_name = os.path.splitext(image)\n", - "\n", - " if model_choice == \"Own_model\":\n", - " masks, flows, styles = model.eval(stack, diameter=Object_diameter, channels=channels, do_3D=True, anisotropy = Anisotropy, min_size=Min_size, cellprob_threshold=mask_threshold)\n", - " else:\n", - " masks, flows, styles, diams = model.eval(stack, diameter=Object_diameter, channels=channels, do_3D=True, anisotropy = Anisotropy, min_size=Min_size, cellprob_threshold=mask_threshold)\n", - "\n", - " prediction_stack_32 = img_as_float32(masks, force_copy=False)\n", - " os.chdir(Result_folder)\n", - " imsave(str(short_name[0])+\".tif\", prediction_stack_32)\n", - "\n", - "\n", - " def f(Z_plane):\n", - " flowi = flows[0]\n", - " fig = plt.figure(figsize=(20,10))\n", - " plot.show_segmentation(fig, stack[Z_plane], masks[Z_plane], flowi[Z_plane], channels=channels)\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - " print('--------------------------------------------------------------')\n", - " interact(f, Z_plane=widgets.IntSlider(min=0, max=masks.shape[0], step=1, value=0));\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "hvkd66PldsXB" - }, - "source": [ - "## **6.3. Download your predictions**\n", - "---\n", - "\n", - "**Store your data** and ALL its results elsewhere by downloading it from Google Drive and after that clean the original folder tree (datasets, results, trained model etc.) if you plan to train or use new networks. Please note that the notebook will otherwise **OVERWRITE** all files which have the same name." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "dHUKCoSZ7dzV" - }, - "source": [ - "# **7. Version log**\n", - "---\n", - "**v1.16.3**:\n", - "* Cyto3 added\n", - "* --fast mode removed\n", - "\n", - "**v1.16.2**:\n", - "* Debug in load a model or run QC with the current model (list of files avoids pdf and QC_model is checked rather than the model type)\n", - "\n", - "**v1.16.1**:\n", - "* Replaced all absolute pathing with relative pathing\n", - "\n", - "**v1.16**:\n", - "* Cellpose 2.2 is now used by default\n", - "* LiveCell and TissueNet models added\n", - "* A few minor text change\n", - "\n", - "\n", - "**v1.15.4**:\n", - "* ZIP compression when saving label images removed\n", - "\n", - "**v1.15.3**:\n", - "* A few minor text change\n", - "\n", - "**v1.15.2**:\n", - "\n", - "* !pip install cellpose==1.0.2\n", - "\n", - "* add verbose option and fix issues when less than 10 images are used for training.\n", - "\n", - "\n", - "\n", - "**v1.15.1**:\n", - "* !pip install opencv-python-headless==4.1.2.30\n", - "\n", - "\n", - "**v1.15**:\n", - "* Cellpose updated to 0.7.\n", - "* Omnipose bacteria and Cyto2 models added.\n", - "* Various dependencies issues fixed.\n", - "\n", - "\n", - "**v1.14**:\n", - "* Added the possibility to perform 3D predictions (section 6.2).\n", - "\n", - "**v1.13**: \n", - "\n", - "* Training now uses TORCH.\n", - "* This version also now includes built-in version check and the version log that you're reading now." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "u4pcBe8Z3T2J" - }, - "source": [ - "# **Thank you for using Cellpose 2D!**" - ] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "machine_shape": "hm", - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "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": "3.6.4" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file +{"cells":[{"cell_type":"markdown","metadata":{"id":"V9zNGvape2-I"},"source":["# **Cellpose - Omnipose (2D)**\n","\n","---\n","\n"]},{"cell_type":"markdown","metadata":{"id":"pwLsIXtEw3Kx"},"source":["**Cellpose 2D** is a deep-learning method that can be used to segment cell and/or nuclei from bioimages and was first published by [Stringer *et al.* in 2020, in Nature Method](https://www.nature.com/articles/s41592-020-01018-x). Omnipose was developped by [Kevin John Cutler and colleagues](https://www.biorxiv.org/content/10.1101/2021.11.03.467199v2).\n","\n"," **This particular notebook enables segmentation of 2D and 3D datasets. Hovewer, training is limited to 2D images. If you are interested in using 3D datasets for training, you should use the StarDist or U-Net 3D notebooks instead.**\n","\n","---\n","*Disclaimer*:\n","\n","This notebook is part of the Zero-Cost Deep-Learning to Enhance Microscopy project (https://github.com/HenriquesLab/DeepLearning_Collab/wiki). Jointly developed by the Jacquemet (link to https://cellmig.org/) and Henriques (https://henriqueslab.github.io/) laboratories.\n","\n","This notebook is largely based on the paper:\n","\n","**Cellpose: a generalist algorithm for cellular segmentation** from Stringer *et al.*, Nature Methods, 2020. (https://www.nature.com/articles/s41592-020-01018-x)\n","\n","**Omnipose: a high-precision morphology-independent solution for bacterial cell segmentation** from Kevin J. Cutler et al, biorXiv, 2021. (https://www.biorxiv.org/content/10.1101/2021.11.03.467199v2)\n","\n","**The Original code** is freely available in GitHub:\n","https://github.com/MouseLand/cellpose\n","\n","**Please also cite these original papers when using or developing this notebook.**\n","\n","**This notebook was also inspired by the one created by @pr4deepr** which is available here:\n","https://colab.research.google.com/github/MouseLand/cellpose/blob/8dadbe528b1ee3f59533bc23ab0ed371d5e7b769/notebooks/Cellpose_2D_v0_1.ipynb\n"]},{"cell_type":"markdown","metadata":{"id":"C5oYf0Q5yXrl"},"source":["# **0. Before getting started**\n","---\n"," For Cellpose to train, **it needs to have access to a paired training dataset made of images and their corresponding masks (label images)**. Information on how to generate a training dataset is available in our Wiki page: https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki\n","\n","**We strongly recommend that you generate extra paired images. These images can be used to assess the quality of your trained model**. The quality control assessment can be done directly in this notebook.\n","\n","The data structure is important. It is necessary that all the input data are in the same folder and that all the output data is in a separate folder. The provided training dataset is already split in two folders called \"Training - Images\" (Training_source) and \"Training - Masks\" (Training_target).\n","\n","Additionally, the corresponding Training_source and Training_target files need to have **the same name**.\n","\n"," **Use 8/16 bit png or Tiff images**.\n","\n","\n","You can also provide a folder that contains the data that you wish to analyse with the trained network once all training has been performed. This can include Test dataset for which you have the equivalent output and can compare to what the network provides.\n","\n","Here's a common data structure that can work:\n","* Experiment A\n"," - **Training dataset**\n"," - Images (Training_source)\n"," - img_1.tif, img_2.tif, ...\n"," - Label images (Training_target)\n"," - img_1.tif, img_2.tif, ...\n"," - **Quality control dataset**\n"," - Images\n"," - img_1.tif, img_2.tif\n"," - Masks\n"," - img_1.tif, img_2.tif\n"," - **Data to be predicted**\n"," - **Results**\n","\n","---\n","**Important note**\n","\n","- If you wish to **Train a network from scratch** using your own dataset (and we encourage everyone to do that), you will need to run **sections 1 - 4**, then use **section 5** to assess the quality of your model and **section 6** to run predictions using the model that you trained.\n","\n","- If you wish to **Evaluate your model** using a model previously generated and saved on your Google Drive, you will only need to run **sections 1 and 2** to set up the notebook, then use **section 5** to assess the quality of your model.\n","\n","- If you only wish to **run predictions** using a pretrained model you will only need to run **sections 1 and 2** to set up the notebook, then use **section 6** to run the predictions on the desired model.\n","---"]},{"cell_type":"markdown","metadata":{"id":"n4yWFoJNnoin"},"source":["# **1. Install Cellpose and dependencies**\n","---\n"]},{"cell_type":"markdown","metadata":{"id":"WlPYHrPHlUtl"},"source":["\n","## **1.1. Install dependencies**\n","---\n","\n","\n","\n"]},{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":199645,"status":"ok","timestamp":1744793412834,"user":{"displayName":"guillaume Jacquemet","userId":"03282992684407736974"},"user_tz":-180},"id":"3u2mXn3XsWzd","outputId":"bfc2c1a8-b13d-4d9c-b457-15b38edbf550"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/70.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m70.0/70.0 kB\u001b[0m \u001b[31m1.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Building wheel for folium (setup.py) ... \u001b[?25l\u001b[?25hdone\n","\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","datascience 0.17.6 requires folium>=0.9.1, but you have folium 0.2.1 which is incompatible.\n","geemap 0.35.3 requires folium>=0.17.0, but you have folium 0.2.1 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m562.7/562.7 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Building wheel for imgaug (setup.py) ... \u001b[?25l\u001b[?25hdone\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.0/61.0 kB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m18.3/18.3 MB\u001b[0m \u001b[31m104.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","datascience 0.17.6 requires folium>=0.9.1, but you have folium 0.2.1 which is incompatible.\n","geemap 0.35.3 requires folium>=0.17.0, but you have folium 0.2.1 which is incompatible.\n","thinc 8.3.6 requires numpy<3.0.0,>=2.0.0, but you have numpy 1.26.4 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[0mCollecting opencv-python-headless<4.3\n"," Downloading opencv_python_headless-3.4.18.65-cp36-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (18 kB)\n","Requirement already satisfied: numpy>=1.21.2 in /usr/local/lib/python3.11/dist-packages (from opencv-python-headless<4.3) (1.26.4)\n","Downloading opencv_python_headless-3.4.18.65-cp36-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (45.7 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.7/45.7 MB\u001b[0m \u001b[31m51.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: opencv-python-headless\n"," Attempting uninstall: opencv-python-headless\n"," Found existing installation: opencv-python-headless 4.11.0.86\n"," Uninstalling opencv-python-headless-4.11.0.86:\n"," Successfully uninstalled opencv-python-headless-4.11.0.86\n","\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","albumentations 2.0.5 requires opencv-python-headless>=4.9.0.80, but you have opencv-python-headless 3.4.18.65 which is incompatible.\n","albucore 0.0.23 requires opencv-python-headless>=4.9.0.80, but you have opencv-python-headless 3.4.18.65 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[0mSuccessfully installed opencv-python-headless-3.4.18.65\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m225.9/225.9 kB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m363.4/363.4 MB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.8/13.8 MB\u001b[0m \u001b[31m117.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 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/usr/local/lib/python3.11/dist-packages/opencv_python.libs/libXau-00ec42fe.so.6.0.0\n"," /usr/local/lib/python3.11/dist-packages/opencv_python.libs/libavcodec-76c43bf0.so.59.37.100\n"," /usr/local/lib/python3.11/dist-packages/opencv_python.libs/libavformat-d296e685.so.59.27.100\n"," /usr/local/lib/python3.11/dist-packages/opencv_python.libs/libavutil-734d06dd.so.57.28.100\n"," /usr/local/lib/python3.11/dist-packages/opencv_python.libs/libcrypto-8c1ab3ad.so.1.1\n"," /usr/local/lib/python3.11/dist-packages/opencv_python.libs/libgfortran-91cc3cb1.so.3.0.0\n"," /usr/local/lib/python3.11/dist-packages/opencv_python.libs/libopenblas-r0-f650aae0.3.3.so\n"," /usr/local/lib/python3.11/dist-packages/opencv_python.libs/libpng16-ef62451c.so.16.44.0\n"," /usr/local/lib/python3.11/dist-packages/opencv_python.libs/libquadmath-96973f99.so.0.0.0\n"," /usr/local/lib/python3.11/dist-packages/opencv_python.libs/libssl-28bef1ac.so.1.1\n"," /usr/local/lib/python3.11/dist-packages/opencv_python.libs/libswresample-3e7db482.so.4.7.100\n"," /usr/local/lib/python3.11/dist-packages/opencv_python.libs/libswscale-95ddd674.so.6.7.100\n"," /usr/local/lib/python3.11/dist-packages/opencv_python.libs/libvpx-9f572e11.so.9.1.0\n"," /usr/local/lib/python3.11/dist-packages/opencv_python.libs/libxcb-icccm-413c9f41.so.4.0.0\n"," /usr/local/lib/python3.11/dist-packages/opencv_python.libs/libxcb-image-e82a276d.so.0.0.0\n"," /usr/local/lib/python3.11/dist-packages/opencv_python.libs/libxcb-keysyms-21015570.so.1.0.0\n"," /usr/local/lib/python3.11/dist-packages/opencv_python.libs/libxcb-randr-a96a5a87.so.0.1.0\n"," /usr/local/lib/python3.11/dist-packages/opencv_python.libs/libxcb-render-637b984a.so.0.0.0\n"," /usr/local/lib/python3.11/dist-packages/opencv_python.libs/libxcb-render-util-43ce00f5.so.0.0.0\n"," /usr/local/lib/python3.11/dist-packages/opencv_python.libs/libxcb-shape-25c2b258.so.0.0.0\n"," /usr/local/lib/python3.11/dist-packages/opencv_python.libs/libxcb-shm-7a199f70.so.0.0.0\n"," /usr/local/lib/python3.11/dist-packages/opencv_python.libs/libxcb-sync-89374f40.so.1.0.0\n"," /usr/local/lib/python3.11/dist-packages/opencv_python.libs/libxcb-util-4d666913.so.1.0.0\n"," /usr/local/lib/python3.11/dist-packages/opencv_python.libs/libxcb-xfixes-9be3ba6f.so.0.0.0\n"," /usr/local/lib/python3.11/dist-packages/opencv_python.libs/libxcb-xinerama-ae147f87.so.0.0.0\n"," /usr/local/lib/python3.11/dist-packages/opencv_python.libs/libxcb-xkb-9ba31ab3.so.1.0.0\n"," /usr/local/lib/python3.11/dist-packages/opencv_python.libs/libxkbcommon-71ae2972.so.0.0.0\n"," /usr/local/lib/python3.11/dist-packages/opencv_python.libs/libxkbcommon-x11-c65ed502.so.0.0.0\n"," Would not remove (might be manually added):\n"," /usr/local/lib/python3.11/dist-packages/cv2/bgsegm/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/bioinspired/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/ccm/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/colored_kinfu/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/data/haarcascade_licence_plate_rus_16stages.xml\n"," /usr/local/lib/python3.11/dist-packages/cv2/datasets/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/dnn_superres/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/dpm/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/dynafu/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/face/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/ft/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/hfs/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/img_hash/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/intensity_transform/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/kinfu/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/kinfu/detail/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/large_kinfu/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/legacy/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/line_descriptor/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/linemod/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/mcc/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/motempl/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/multicalib/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/omnidir/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/optflow/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/phase_unwrapping/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/plot/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/ppf_match_3d/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/quality/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/rapid/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/reg/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/rgbd/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/saliency/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/signal/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/stereo/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/structured_light/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/text/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/videostab/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/wechat_qrcode/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/xfeatures2d/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/ximgproc/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/ximgproc/segmentation/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/xphoto/__init__.pyi\n","Proceed (Y/n)? Y\n"," Successfully uninstalled opencv-python-4.11.0.86\n","Found existing installation: opencv-contrib-python 4.11.0.86\n","Uninstalling opencv-contrib-python-4.11.0.86:\n"," Would remove:\n"," /usr/local/lib/python3.11/dist-packages/cv2/bgsegm/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/bioinspired/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/ccm/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/colored_kinfu/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/datasets/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/dnn_superres/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/dpm/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/dynafu/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/face/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/ft/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/hfs/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/img_hash/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/intensity_transform/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/kinfu/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/kinfu/detail/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/large_kinfu/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/legacy/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/line_descriptor/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/linemod/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/mcc/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/motempl/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/multicalib/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/omnidir/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/optflow/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/phase_unwrapping/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/plot/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/ppf_match_3d/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/quality/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/rapid/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/reg/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/rgbd/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/saliency/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/signal/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/stereo/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/structured_light/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/text/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/videostab/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/wechat_qrcode/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/xfeatures2d/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/ximgproc/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/ximgproc/segmentation/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/cv2/xphoto/__init__.pyi\n"," /usr/local/lib/python3.11/dist-packages/opencv_contrib_python-4.11.0.86.dist-info/*\n"," /usr/local/lib/python3.11/dist-packages/opencv_contrib_python.libs/libQt5Core-e7f476e2.so.5.15.16\n"," /usr/local/lib/python3.11/dist-packages/opencv_contrib_python.libs/libQt5Gui-1ce8cbfe.so.5.15.16\n"," /usr/local/lib/python3.11/dist-packages/opencv_contrib_python.libs/libQt5Test-9ac3ed15.so.5.15.16\n"," /usr/local/lib/python3.11/dist-packages/opencv_contrib_python.libs/libQt5Widgets-cd430389.so.5.15.16\n"," /usr/local/lib/python3.11/dist-packages/opencv_contrib_python.libs/libQt5XcbQpa-b18b5a78.so.5.15.16\n"," /usr/local/lib/python3.11/dist-packages/opencv_contrib_python.libs/libX11-xcb-0e257303.so.1.0.0\n"," /usr/local/lib/python3.11/dist-packages/opencv_contrib_python.libs/libXau-00ec42fe.so.6.0.0\n"," /usr/local/lib/python3.11/dist-packages/opencv_contrib_python.libs/libavcodec-76c43bf0.so.59.37.100\n"," /usr/local/lib/python3.11/dist-packages/opencv_contrib_python.libs/libavformat-d296e685.so.59.27.100\n"," /usr/local/lib/python3.11/dist-packages/opencv_contrib_python.libs/libavutil-734d06dd.so.57.28.100\n"," /usr/local/lib/python3.11/dist-packages/opencv_contrib_python.libs/libcrypto-8c1ab3ad.so.1.1\n"," /usr/local/lib/python3.11/dist-packages/opencv_contrib_python.libs/libgfortran-91cc3cb1.so.3.0.0\n"," /usr/local/lib/python3.11/dist-packages/opencv_contrib_python.libs/libopenblas-r0-f650aae0.3.3.so\n"," /usr/local/lib/python3.11/dist-packages/opencv_contrib_python.libs/libpng16-ef62451c.so.16.44.0\n"," /usr/local/lib/python3.11/dist-packages/opencv_contrib_python.libs/libquadmath-96973f99.so.0.0.0\n"," /usr/local/lib/python3.11/dist-packages/opencv_contrib_python.libs/libssl-28bef1ac.so.1.1\n"," /usr/local/lib/python3.11/dist-packages/opencv_contrib_python.libs/libswresample-3e7db482.so.4.7.100\n"," /usr/local/lib/python3.11/dist-packages/opencv_contrib_python.libs/libswscale-95ddd674.so.6.7.100\n"," /usr/local/lib/python3.11/dist-packages/opencv_contrib_python.libs/libvpx-9f572e11.so.9.1.0\n"," /usr/local/lib/python3.11/dist-packages/opencv_contrib_python.libs/libxcb-icccm-413c9f41.so.4.0.0\n"," /usr/local/lib/python3.11/dist-packages/opencv_contrib_python.libs/libxcb-image-e82a276d.so.0.0.0\n"," /usr/local/lib/python3.11/dist-packages/opencv_contrib_python.libs/libxcb-keysyms-21015570.so.1.0.0\n"," /usr/local/lib/python3.11/dist-packages/opencv_contrib_python.libs/libxcb-randr-a96a5a87.so.0.1.0\n"," /usr/local/lib/python3.11/dist-packages/opencv_contrib_python.libs/libxcb-render-637b984a.so.0.0.0\n"," /usr/local/lib/python3.11/dist-packages/opencv_contrib_python.libs/libxcb-render-util-43ce00f5.so.0.0.0\n"," /usr/local/lib/python3.11/dist-packages/opencv_contrib_python.libs/libxcb-shape-25c2b258.so.0.0.0\n"," /usr/local/lib/python3.11/dist-packages/opencv_contrib_python.libs/libxcb-shm-7a199f70.so.0.0.0\n"," /usr/local/lib/python3.11/dist-packages/opencv_contrib_python.libs/libxcb-sync-89374f40.so.1.0.0\n"," /usr/local/lib/python3.11/dist-packages/opencv_contrib_python.libs/libxcb-util-4d666913.so.1.0.0\n"," /usr/local/lib/python3.11/dist-packages/opencv_contrib_python.libs/libxcb-xfixes-9be3ba6f.so.0.0.0\n"," /usr/local/lib/python3.11/dist-packages/opencv_contrib_python.libs/libxcb-xinerama-ae147f87.so.0.0.0\n"," /usr/local/lib/python3.11/dist-packages/opencv_contrib_python.libs/libxcb-xkb-9ba31ab3.so.1.0.0\n"," /usr/local/lib/python3.11/dist-packages/opencv_contrib_python.libs/libxkbcommon-71ae2972.so.0.0.0\n"," /usr/local/lib/python3.11/dist-packages/opencv_contrib_python.libs/libxkbcommon-x11-c65ed502.so.0.0.0\n","Proceed (Y/n)? Y\n"," Successfully uninstalled opencv-contrib-python-4.11.0.86\n","Collecting opencv-python\n"," Downloading opencv_python-4.11.0.86-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (20 kB)\n","Requirement already satisfied: numpy>=1.21.2 in /usr/local/lib/python3.11/dist-packages (from opencv-python) (1.26.4)\n","Downloading opencv_python-4.11.0.86-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (63.0 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m63.0/63.0 MB\u001b[0m \u001b[31m39.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: opencv-python\n","Successfully installed opencv-python-4.11.0.86\n","Collecting opencv-contrib-python\n"," Downloading opencv_contrib_python-4.11.0.86-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (20 kB)\n","Requirement already satisfied: numpy>=1.21.2 in /usr/local/lib/python3.11/dist-packages (from opencv-contrib-python) (1.26.4)\n","Downloading opencv_contrib_python-4.11.0.86-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (69.1 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m69.1/69.1 MB\u001b[0m \u001b[31m36.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: opencv-contrib-python\n","Successfully installed opencv-contrib-python-4.11.0.86\n"]}],"source":["\n","#@markdown ##Install Cellpose\n","\n","#Libraries contains information of certain topics.\n","#For example the tifffile library contains information on how to handle tif-files.\n","\n","#Here, we install libraries which are not already included in Colab.\n","\n","!pip uninstall -y -q yellowbrick\n","\n","!pip install -q tifffile # contains tools to operate tiff-files\n","!pip install -q folium==0.2.1\n","!pip install -q imgaug==0.2.5\n","!pip install -q numpy==1.26.4\n","!pip install \"opencv-python-headless<4.3\"\n","!pip install -q cellpose\n","!pip install -q wget\n","!pip install -q memory_profiler\n","!pip install -q fpdf2\n","\n","# --- suggested changes ---\n","!pip uninstall opencv-python opencv-contrib-python # Uninstall conflicting packages\n","!pip install opencv-python # Reinstall opencv-python, potentially with ==4.8.1 if issues persist\n","!pip install opencv-contrib-python # Reinstall the contrib package if needed\n","# --- end of suggested changes ---\n","\n","#Force session restart\n","exit(0)\n","\n"]},{"cell_type":"markdown","metadata":{"id":"w-FE-qZI5U_k"},"source":["\n","## **1.2. Restart your runtime**\n","---\n","\n","\n","\n","** Ignore the following message error message. Your Runtime has automatically restarted. This is normal.**\n","\n","\"\"
\n","\n","\n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"wgeU4cC55Sej"},"source":["## **1.3. Load key dependencies**\n","---\n",""]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3012,"status":"ok","timestamp":1744793439612,"user":{"displayName":"guillaume Jacquemet","userId":"03282992684407736974"},"user_tz":-180},"id":"Kkbr0GAA5ZAQ","outputId":"c952d975-a16c-421f-f518-0ef09ca6e147","cellView":"form"},"outputs":[{"output_type":"stream","name":"stdout","text":["The memory_profiler extension is already loaded. To reload it, use:\n"," %reload_ext memory_profiler\n","Libraries installed\n","Notebook version: 1.16.3\n","Latest notebook version: 1.16.3\n","This notebook is up-to-date.\n"]}],"source":["#@markdown ##Load key dependencies\n","\n","Notebook_version = '1.16.3'\n","Network = 'Cellpose'\n","\n","from builtins import any as b_any\n","\n","%load_ext memory_profiler\n","\n","def get_requirements_path():\n"," # Store requirements file in 'contents' directory\n"," current_dir = os.getcwd()\n"," dir_count = current_dir.count('/') - 1\n"," path = '../' * (dir_count) + 'requirements.txt'\n"," return path\n","\n","def filter_files(file_list, filter_list):\n"," filtered_list = []\n"," for fname in file_list:\n"," if b_any(fname.split('==')[0] in s for s in filter_list):\n"," filtered_list.append(fname)\n"," return filtered_list\n","\n","def build_requirements_file(before, after):\n"," path = get_requirements_path()\n","\n"," # Exporting requirements.txt for local run\n"," !pip freeze > $path\n","\n"," # Get minimum requirements file\n"," df = pd.read_csv(path)\n"," mod_list = [m.split('.')[0] for m in after if not m in before]\n"," req_list_temp = df.values.tolist()\n"," req_list = [x[0] for x in req_list_temp]\n","\n"," # Replace with package name and handle cases where import name is different to module name\n"," mod_name_list = [['sklearn', 'scikit-learn'], ['skimage', 'scikit-image']]\n"," mod_replace_list = [[x[1] for x in mod_name_list] if s in [x[0] for x in mod_name_list] else s for s in mod_list]\n"," filtered_list = filter_files(req_list, mod_replace_list)\n","\n"," file=open(path,'w')\n"," for item in filtered_list:\n"," file.writelines(item + '\\n')\n","\n"," file.close()\n","\n","import sys\n","before = [str(m) for m in sys.modules]\n","\n","\n","\n","# ------- Variable specific to Cellpose -------\n","\n","from urllib.parse import urlparse\n","%matplotlib inline\n","from cellpose import models\n","#use_GPU = models.use_gpu()\n","\n","#import mxnet as mx\n","\n","from skimage.util import img_as_ubyte\n","import cv2\n","from cellpose import plot\n","from ipywidgets import interact, interact_manual\n","from zipfile import ZIP_DEFLATED\n","\n","\n","# For sliders and dropdown menu and progress bar\n","from ipywidgets import interact\n","import ipywidgets as widgets\n","\n","\n","# ------- Common variable to all ZeroCostDL4Mic notebooks -------\n","import numpy as np\n","from matplotlib import pyplot as plt\n","import urllib\n","import os, random\n","import shutil\n","import zipfile\n","from tifffile import imread, imwrite as imsave\n","import time\n","import sys\n","import wget\n","from pathlib import Path\n","import pandas as pd\n","import csv\n","from glob import glob\n","from scipy import signal\n","from scipy import ndimage\n","from skimage import io\n","from sklearn.linear_model import LinearRegression\n","from skimage.util import img_as_uint\n","import matplotlib as mpl\n","from skimage.metrics import structural_similarity\n","from skimage.metrics import peak_signal_noise_ratio as psnr\n","from astropy.visualization import simple_norm\n","from skimage import img_as_float32\n","from skimage.util import img_as_ubyte\n","from tqdm import tqdm\n","from fpdf import FPDF, HTMLMixin\n","from datetime import datetime\n","import subprocess\n","from pip._internal.operations.freeze import freeze\n","\n","\n","#Create a variable to get and store relative base path\n","base_path = os.getcwd()\n","\n","# Colors for the warning messages\n","class bcolors:\n"," WARNING = '\\033[31m'\n","\n","W = '\\033[0m' # white (normal)\n","R = '\\033[31m' # red\n","\n","#Disable some of the tensorflow warnings\n","import warnings\n","warnings.filterwarnings(\"ignore\")\n","\n","print(\"Libraries installed\")\n","\n","# Check if this is the latest version of the notebook\n","All_notebook_versions = pd.read_csv(\"https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/Latest_Notebook_versions.csv\", dtype=str)\n","print('Notebook version: '+Notebook_version)\n","Latest_Notebook_version = All_notebook_versions[All_notebook_versions[\"Notebook\"] == Network]['Version'].iloc[0]\n","print('Latest notebook version: '+Latest_Notebook_version)\n","if Notebook_version == Latest_Notebook_version:\n"," print(\"This notebook is up-to-date.\")\n","else:\n"," print(bcolors.WARNING +\"A new version of this notebook has been released. We recommend that you download it at https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki\")\n","\n","\n","!pip freeze > requirements.txt\n","\n","#Create a pdf document with training summary\n","\n","def pdf_export(trained = False, augmentation = False, pretrained_model = False):\n"," # save FPDF() class into a\n"," # variable pdf\n"," #from datetime import datetime\n","\n"," class MyFPDF(FPDF, HTMLMixin):\n"," pass\n","\n"," pdf = MyFPDF()\n"," pdf.add_page()\n"," pdf.set_right_margin(-1)\n"," pdf.set_font(\"Arial\", size = 11, style='B')\n","\n","\n"," day = datetime.now()\n"," datetime_str = str(day)[0:10]\n","\n"," Header = 'Training report for '+Network+' model ('+model_name+')\\nDate: '+datetime_str\n"," pdf.multi_cell(180, 5, txt = Header, align = 'L')\n","\n"," # add another cell\n"," if trained:\n"," training_time = \"Training time: \"+str(hour)+ \"hour(s) \"+str(mins)+\"min(s) \"+str(round(sec))+\"sec(s)\"\n"," pdf.cell(190, 5, txt = training_time, ln = 1, align='L')\n"," pdf.ln(1)\n","\n"," Header_2 = 'Information for your materials and methods:'\n"," pdf.cell(190, 5, txt=Header_2, ln=1, align='L')\n","\n"," all_packages = ''\n"," for requirement in freeze(local_only=True):\n"," all_packages = all_packages+requirement+', '\n"," #print(all_packages)\n","\n"," #Main Packages\n"," main_packages = ''\n"," version_numbers = []\n"," for name in ['tensorflow','numpy','Keras','csbdeep']:\n"," find_name=all_packages.find(name)\n"," main_packages = main_packages+all_packages[find_name:all_packages.find(',',find_name)]+', '\n"," #Version numbers only here:\n"," version_numbers.append(all_packages[find_name+len(name)+2:all_packages.find(',',find_name)])\n","\n"," try:\n"," cuda_version = subprocess.run([\"nvcc\",\"--version\"],stdout=subprocess.PIPE)\n"," cuda_version = cuda_version.stdout.decode('utf-8')\n"," cuda_version = cuda_version[cuda_version.find(', V')+3:-1]\n"," except:\n"," cuda_version = ' - No cuda found - '\n"," try:\n"," gpu_name = subprocess.run([\"nvidia-smi\"],stdout=subprocess.PIPE)\n"," gpu_name = gpu_name.stdout.decode('utf-8')\n"," gpu_name = gpu_name[gpu_name.find('Tesla'):gpu_name.find('Tesla')+10]\n"," except:\n"," gpu_name = ' - No GPU found - '\n"," #print(cuda_version[cuda_version.find(', V')+3:-1])\n"," #print(gpu_name)\n","\n"," shape = io.imread(Training_source+'/'+os.listdir(Training_source)[1]).shape\n"," dataset_size = len(os.listdir(Training_source))\n","\n"," text = 'The '+Network+' model was trained from scratch for '+str(number_of_epochs)+' epochs on '+str(dataset_size)+' paired image patches (image dimensions: '+str(shape)+', with a batch size of '+str(batch_size)+', using the '+Network+' ZeroCostDL4Mic notebook (v '+Notebook_version[0]+') (von Chamier & Laine et al., 2020). Key python packages used include tensorflow (v '+version_numbers[0]+'), Keras (v '+version_numbers[2]+'), numpy (v '+version_numbers[1]+'), cuda (v '+cuda_version+'). The training was accelerated using a '+gpu_name+'GPU.'\n","\n","\n"," if pretrained_model:\n"," text = 'The '+Network+' model was trained for '+str(number_of_epochs)+' epochs on '+str(dataset_size)+' paired image patches (image dimensions: '+str(shape)+', with a batch size of '+str(batch_size)+', using the '+Network+' ZeroCostDL4Mic notebook (v '+Notebook_version[0]+') (von Chamier & Laine et al., 2020). The model was re-trained from a pretrained model. Key python packages used include tensorflow (v '+version_numbers[0]+'), Keras (v '+version_numbers[2]+'), numpy (v '+version_numbers[1]+'), cuda (v '+cuda_version+'). The training was accelerated using a '+gpu_name+'GPU.'\n","\n"," # print(text)\n","\n"," pdf.set_font('')\n"," pdf.set_font_size(10.)\n"," pdf.multi_cell(190, 5, txt = text, align='L')\n"," pdf.set_font('')\n"," pdf.set_font('Arial', size = 10, style = 'B')\n"," pdf.ln(1)\n"," pdf.cell(28, 5, txt='Augmentation: ', ln=0)\n"," pdf.set_font('')\n"," if augmentation:\n"," aug_text = 'The dataset was augmented by a factor of '+str(Multiply_dataset_by)\n","\n"," else:\n"," aug_text = 'No augmentation was used for training.'\n"," pdf.multi_cell(190, 5, txt=aug_text, align='L')\n"," pdf.set_font('Arial', size = 11, style = 'B')\n"," pdf.ln(1)\n"," pdf.cell(180, 5, txt = 'Parameters', align='L', ln=1)\n"," pdf.set_font('')\n"," pdf.set_font_size(10.)\n"," if Use_Default_Advanced_Parameters:\n"," pdf.cell(200, 5, txt='Default Advanced Parameters were enabled')\n"," pdf.cell(200, 5, txt='The following parameters were used for training:')\n"," pdf.ln(1)\n"," html = \"\"\"\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
ParameterValue
number_of_epochs{0}
batch_size{1}
percentage_validation{2}
initial_learning_rate{3}
\n"," \"\"\".format(number_of_epochs,batch_size,percentage_validation,initial_learning_rate)\n"," pdf.write_html(html)\n","\n"," #pdf.multi_cell(190, 5, txt = text_2, align='L')\n"," pdf.set_font(\"Arial\", size = 11, style='B')\n"," pdf.ln(1)\n"," pdf.cell(190, 5, txt = 'Training Dataset', align='L', ln=1)\n"," pdf.set_font('')\n"," pdf.set_font('Arial', size = 10, style = 'B')\n"," pdf.cell(29, 5, txt= 'Training_source:', align = 'L', ln=0)\n"," pdf.set_font('')\n"," pdf.multi_cell(170, 5, txt = Training_source, align = 'L')\n"," pdf.set_font('')\n"," pdf.set_font('Arial', size = 10, style = 'B')\n"," pdf.cell(27, 5, txt= 'Training_target:', align = 'L', ln=0)\n"," pdf.set_font('')\n"," pdf.multi_cell(170, 5, txt = Training_target, align = 'L')\n"," #pdf.cell(190, 5, txt=aug_text, align='L', ln=1)\n"," pdf.ln(1)\n"," pdf.set_font('')\n"," pdf.set_font('Arial', size = 10, style = 'B')\n"," pdf.cell(22, 5, txt= 'Model Path:', align = 'L', ln=0)\n"," pdf.set_font('')\n"," pdf.multi_cell(170, 5, txt = model_path+'/'+model_name, align = 'L')\n"," pdf.ln(1)\n"," pdf.cell(60, 5, txt = 'Example Training pair', ln=1)\n"," pdf.ln(1)\n"," exp_size = io.imread(base_path + '/TrainingDataExample_Cellpose2D.png').shape\n"," pdf.image(base_path + '/TrainingDataExample_Cellpose2D.png', x = 11, y = None, w = round(exp_size[1]/8), h = round(exp_size[0]/8))\n"," pdf.ln(1)\n"," ref_1 = 'References:\\n - ZeroCostDL4Mic: von Chamier, Lucas & Laine, Romain, et al. \"ZeroCostDL4Mic: an open platform to simplify access and use of Deep-Learning in Microscopy.\" BioRxiv (2020).'\n"," pdf.multi_cell(190, 5, txt = ref_1, align='L')\n"," ref_2 = '- Cellpose: Stringer, Carsen, et al. \"Cellpose: a generalist algorithm for cellular segmentation.\" Nature Methods 18, pages100-106(2021).'\n"," pdf.multi_cell(190, 5, txt = ref_2, align='L')\n"," pdf.ln(3)\n"," reminder = 'Important:\\nRemember to perform the quality control step on all newly trained models\\nPlease consider depositing your training dataset on Zenodo'\n"," pdf.set_font('')\n"," pdf.set_font('Arial', size = 11, style='B')\n"," pdf.multi_cell(190, 5, txt=reminder, align='C')\n","\n"," pdf.output(model_path+'/'+model_name+'/'+model_name+\"_training_report.pdf\")\n"," # pdf.output(Saving_path+'/train_folder/models/'+model_name+\"_training_report.pdf\")\n","\n","\n","\n","#Make a pdf summary of the QC results\n","\n","def qc_pdf_export():\n"," class MyFPDF(FPDF, HTMLMixin):\n"," pass\n","\n"," pdf = MyFPDF()\n"," pdf.add_page()\n"," pdf.set_right_margin(-1)\n"," pdf.set_font('')\n"," pdf.set_font(\"Arial\", size = 11, style='B')\n","\n"," Network = 'Cellpose 2D'\n"," #model_name = os.path.basename(full_QC_model_path)\n"," day = datetime.now()\n"," datetime_str = str(day)[0:10]\n","\n"," Header = 'Quality Control report for '+Network+' model ('+QC_model_name+')\\nDate: '+datetime_str\n"," pdf.multi_cell(180, 5, txt = Header, align = 'L')\n","\n"," all_packages = ''\n"," for requirement in freeze(local_only=True):\n"," all_packages = all_packages+requirement+', '\n","\n"," pdf.set_font('')\n"," pdf.set_font('Arial', size = 11, style = 'B')\n"," pdf.ln(2)\n"," pdf.cell(190, 5, txt = 'Development of Training Losses', ln=1, align='L')\n"," pdf.ln(1)\n"," exp_size = io.imread(full_QC_model_path+'Quality Control/QC_example_data.png').shape\n"," if os.path.exists(full_QC_model_path+'Quality Control/lossCurvePlots.png'):\n"," pdf.image(full_QC_model_path+'Quality Control/lossCurvePlots.png', x = 11, y = None, w = round(exp_size[1]/10), h = round(exp_size[0]/13))\n"," else:\n"," pdf.set_font('')\n"," pdf.set_font('Arial', size=10)\n"," pdf.multi_cell(190, 5, txt='If you would like to see the evolution of the loss function during training please play the first cell of the QC section in the notebook.', align='L')\n"," pdf.ln(2)\n"," pdf.set_font('')\n"," pdf.set_font('Arial', size = 10, style = 'B')\n"," pdf.ln(3)\n"," pdf.cell(80, 5, txt = 'Example Quality Control Visualisation', ln=1)\n"," pdf.ln(1)\n"," exp_size = io.imread(full_QC_model_path+'Quality Control/QC_example_data.png').shape\n"," pdf.image(full_QC_model_path+'Quality Control/QC_example_data.png', x = 16, y = None, w = round(exp_size[1]/10), h = round(exp_size[0]/10))\n"," pdf.ln(1)\n"," pdf.set_font('')\n"," pdf.set_font('Arial', size = 11, style = 'B')\n"," pdf.ln(1)\n"," pdf.cell(180, 5, txt = 'Quality Control Metrics', align='L', ln=1)\n"," pdf.set_font('')\n"," pdf.set_font_size(10.)\n","\n"," pdf.ln(1)\n"," html = \"\"\"\n"," \n"," \n"," \"\"\"\n"," with open(full_QC_model_path+'Quality Control/Quality_Control for '+QC_model_name+'.csv', 'r') as csvfile:\n"," metrics = csv.reader(csvfile)\n"," header = next(metrics)\n"," image = header[0]\n"," mSSIM_PvsGT = header[1]\n"," mSSIM_SvsGT = header[2]\n"," NRMSE_PvsGT = header[3]\n"," NRMSE_SvsGT = header[4]\n"," PSNR_PvsGT = header[5]\n"," PSNR_SvsGT = header[6]\n"," header = \"\"\"\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \"\"\".format(image,mSSIM_PvsGT,mSSIM_SvsGT,NRMSE_PvsGT,NRMSE_SvsGT,PSNR_PvsGT,PSNR_SvsGT)\n"," html = html+header\n"," for row in metrics:\n"," image = row[0]\n"," mSSIM_PvsGT = row[1]\n"," mSSIM_SvsGT = row[2]\n"," NRMSE_PvsGT = row[3]\n"," NRMSE_SvsGT = row[4]\n"," PSNR_PvsGT = row[5]\n"," PSNR_SvsGT = row[6]\n"," cells = \"\"\"\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \"\"\".format(image,str(round(float(mSSIM_PvsGT),3)),str(round(float(mSSIM_SvsGT),3)),str(round(float(NRMSE_PvsGT),3)),str(round(float(NRMSE_SvsGT),3)),str(round(float(PSNR_PvsGT),3)),str(round(float(PSNR_SvsGT),3)))\n"," html = html+cells\n"," html = html+\"\"\"
{0}{1}{2}{3}{4}{5}{6}
{0}{1}{2}{3}{4}{5}{6}
\"\"\"\n","\n"," pdf.write_html(html)\n","\n"," pdf.ln(1)\n"," pdf.set_font('')\n"," pdf.set_font_size(10.)\n"," ref_1 = 'References:\\n - ZeroCostDL4Mic: von Chamier, Lucas & Laine, Romain, et al. \"ZeroCostDL4Mic: an open platform to simplify access and use of Deep-Learning in Microscopy.\" BioRxiv (2020).'\n"," pdf.multi_cell(190, 5, txt = ref_1, align='L')\n"," ref_2 = '- CARE: Weigert, Martin, et al. \"Content-aware image restoration: pushing the limits of fluorescence microscopy.\" Nature methods 15.12 (2018): 1090-1097.'\n"," pdf.multi_cell(190, 5, txt = ref_2, align='L')\n","\n"," pdf.ln(3)\n"," reminder = 'To find the parameters and other information about how this model was trained, go to the training_report.pdf of this model which should be in the folder of the same name.'\n","\n"," pdf.set_font('Arial', size = 11, style='B')\n"," pdf.multi_cell(190, 5, txt=reminder, align='C')\n","\n"," pdf.output(full_QC_model_path+'Quality Control/'+QC_model_name+'_QC_report.pdf')\n","\n","\n","# Build requirements file for local run\n","after = [str(m) for m in sys.modules]\n","build_requirements_file(before, after)\n"]},{"cell_type":"markdown","metadata":{"id":"b4-r1gE7Iamv"},"source":["# **2. Initialise the Colab session**\n","---"]},{"cell_type":"markdown","metadata":{"id":"DMNHVZfHmbKb"},"source":["\n","## **2.1. Check for GPU access**\n","---\n","\n","By default, the session should be using Python 3 and GPU acceleration, but it is possible to ensure that these are set properly by doing the following:\n","\n","Go to **Runtime -> Change the Runtime type**\n","\n","**Runtime type: Python 3** *(Python 3 is programming language in which this program is written)*\n","\n","**Accelerator: GPU** *(Graphics processing unit)*\n"]},{"cell_type":"code","execution_count":7,"metadata":{"cellView":"form","id":"BDhmUgqCStlm","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1744829046534,"user_tz":-180,"elapsed":105,"user":{"displayName":"guillaume Jacquemet","userId":"03282992684407736974"}},"outputId":"19c353bd-efe5-44be-b080-27da85a3d3c7"},"outputs":[{"output_type":"stream","name":"stdout","text":["You have GPU access\n","Wed Apr 16 18:44:05 2025 \n","+-----------------------------------------------------------------------------------------+\n","| NVIDIA-SMI 550.54.15 Driver Version: 550.54.15 CUDA Version: 12.4 |\n","|-----------------------------------------+------------------------+----------------------+\n","| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\n","| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\n","| | | MIG M. |\n","|=========================================+========================+======================|\n","| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n","| N/A 71C P0 31W / 70W | 8582MiB / 15360MiB | 0% Default |\n","| | | N/A |\n","+-----------------------------------------+------------------------+----------------------+\n"," \n","+-----------------------------------------------------------------------------------------+\n","| Processes: |\n","| GPU GI CI PID Type Process name GPU Memory |\n","| ID ID Usage |\n","|=========================================================================================|\n","+-----------------------------------------------------------------------------------------+\n"]}],"source":["#@markdown ##Run this cell to check if you have GPU access\n","\n","#%tensorflow_version 1.x\n","\n","import tensorflow as tf\n","if tf.test.gpu_device_name()=='':\n"," print('You do not have GPU access.')\n"," print('Did you change your runtime ?')\n"," print('If the runtime setting is correct then Google did not allocate a GPU for your session')\n"," print('Expect slow performance. To access GPU try reconnecting later')\n","\n","else:\n"," print('You have GPU access')\n"," !nvidia-smi"]},{"cell_type":"markdown","metadata":{"id":"-oqBTeLaImnU"},"source":["## **2.2. Mount your Google Drive**\n","---\n"," To use this notebook on the data present in your Google Drive, you need to mount your Google Drive to this notebook.\n","\n"," Play the cell below to mount your Google Drive and follow the instructions.\n","\n"," Once this is done, your data are available in the **Files** tab on the top left of notebook."]},{"cell_type":"code","execution_count":4,"metadata":{"cellView":"form","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1744793467160,"user":{"displayName":"guillaume Jacquemet","userId":"03282992684407736974"},"user_tz":-180},"id":"01Djr8v-5pPk","outputId":"a5ebef4a-b579-465b-e756-d437c9b68d69"},"outputs":[{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["Mounted at /content/gdrive\n"]}],"source":["\n","#@markdown ##Run this cell to connect your Google Drive to Colab\n","\n","#@markdown * Follow the instructions.\n","\n","\n","#@markdown * Click on \"Files\" site on the right. Refresh the site. Your Google Drive folder should now be available here as \"drive\".\n","\n","#mounts user's Google Drive to Google Colab.\n","\n","from google.colab import drive\n","drive.mount('/content/gdrive')\n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"2UfUWjI_askO"},"source":["# **3. Select your parameters and paths**\n","---"]},{"cell_type":"markdown","metadata":{"id":"ZyMxrSWvavVL"},"source":["## **3.1. Setting main training parameters**\n","---\n",""]},{"cell_type":"markdown","metadata":{"id":"5MlTyQVXXvDx"},"source":[" **Paths for training, predictions and results**\n","\n","\n","**`Training_source:`, `Training_target`:** These are the paths to your folders containing the Training_source (images of cells) and Training_target (masks) training data respecively. To find the paths of the folders containing the respective datasets, go to your Files on the left of the notebook, navigate to the folder containing your files and copy the path by right-clicking on the folder, **Copy path** and pasting it into the right box below.\n","\n","**`model_name`:** Use only my_model -style, not my-model (Use \"_\" not \"-\"). Do not use spaces in the name. Avoid using the name of an existing model (saved in the same folder) as it will be overwritten.\n","\n","**`model_path`**: Enter the path where your model will be saved once trained (for instance your result folder).\n","\n","\n","**Training parameters**\n","\n","**`number_of_epochs`:** Input how many epochs (rounds) the network will be trained. Preliminary results can already be observed after a 100 epochs, but a full training should run for up to 500-1000 epochs. Evaluate the performance after training (see 5.). **Default value: 500**\n","\n","**Advanced Parameters - experienced users only**\n","\n","**`batch_size:`** This parameter defines the number of patches seen in each training step. Reducing or increasing the **batch size** may slow or speed up your training, respectively, and can influence network performance. **Default value: 8**\n","\n","\n","**`percentage_validation`:** Input the percentage of your training dataset you want to use to validate the network during the training. **Default value: 10**\n","\n","\n","\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"2HkNZ16BdfJv"},"outputs":[],"source":["#@markdown ###Path to training images:\n","\n","Training_source = \"\" #@param {type:\"string\"}\n","Training_target = \"\" #@param {type:\"string\"}\n","\n","#Define where the patch file will be saved\n","base = base_path\n","\n","# model name and path\n","#@markdown ###Name of the model and path to model folder:\n","model_name = \"\" #@param {type:\"string\"}\n","model_path = \"\" #@param {type:\"string\"}\n","\n","# other parameters for training.\n","#@markdown ###Training Parameters:\n","#@markdown Number of epochs:\n","number_of_epochs = 500#@param {type:\"number\"}\n","\n","Channel_to_use_for_training = \"Red\" #@param [\"Grayscale\", \"Blue\", \"Green\", \"Red\"]\n","\n","# @markdown ###If you have a secondary channel that can be used for training, for instance nuclei, choose it here:\n","\n","Second_training_channel= \"None\" #@param [\"None\", \"Blue\", \"Green\", \"Red\"]\n","\n","\n","#@markdown ###Advanced Parameters\n","\n","Use_Default_Advanced_Parameters = True #@param {type:\"boolean\"}\n","#@markdown ###If not, please input:\n","batch_size = 8#@param {type:\"number\"}\n","initial_learning_rate = 0.0002 #@param {type:\"number\"}\n","percentage_validation = 10#@param {type:\"number\"}\n","\n","if (Use_Default_Advanced_Parameters):\n"," print(\"Default advanced parameters enabled\")\n"," batch_size = 8\n"," initial_learning_rate = 0.0002\n"," percentage_validation = 10\n","\n","#here we check that no model with the same name already exist, if so delete\n","if os.path.exists(model_path+'/'+model_name):\n"," print(bcolors.WARNING +\"!! WARNING: \"+model_name+\" already exists and will be deleted in the following cell !!\")\n"," print(bcolors.WARNING +\"To continue training \"+model_name+\", choose a new model_name here, and load \"+model_name+\" in section 3.3\"+W)\n","\n","# Here we enable the cyto pre-trained model by default (in case the cell is not ran)\n","model_to_load = \"cyto\"\n","# Here we disable data augmentation by default (in case the cell is not ran)\n","\n","Use_Data_augmentation = True\n","\n","# This will display a randomly chosen dataset input and output\n","random_choice = random.choice(os.listdir(Training_source))\n","x = io.imread(Training_source+\"/\"+random_choice)\n","norm = simple_norm(x, percent = 99)\n","y = io.imread(Training_target+\"/\"+random_choice)\n","\n","# Find the number of channel in the input image\n","\n","n_channel = 1 if x.ndim == 2 else x.shape[-1]\n","\n","\n","# Here we match the channel to number\n","\n","if Channel_to_use_for_training == \"Grayscale\":\n"," Training_channel = 0\n","\n"," if not n_channel == 1:\n"," print(bcolors.WARNING +\"!! WARNING: your image has more than one channel, choose which channel you want to use for trainning !!\")\n","\n","if Channel_to_use_for_training == \"Blue\":\n"," Training_channel = 3\n","\n","if Channel_to_use_for_training == \"Green\":\n"," Training_channel = 2\n","\n","if Channel_to_use_for_training == \"Red\":\n"," Training_channel = 1\n","\n","\n","if Second_training_channel == \"Blue\":\n"," Second_training_channel = 3\n","\n","if Second_training_channel == \"Green\":\n"," Second_training_channel = 2\n","\n","if Second_training_channel == \"Red\":\n"," Second_training_channel = 1\n","\n","if Second_training_channel == \"None\":\n"," Second_training_channel = 0\n","\n","\n","if n_channel ==1:\n","\n"," f=plt.figure(figsize=(16,8))\n"," plt.subplot(1,2,1)\n"," plt.imshow(x, norm=norm, cmap='magma', interpolation='nearest')\n"," plt.title('Training source')\n"," plt.axis('off');\n","\n"," plt.subplot(1,2,2)\n"," plt.imshow(y,cmap='nipy_spectral', interpolation='nearest')\n"," plt.title('Training target')\n"," plt.axis('off');\n","\n"," plt.savefig(base_path + '/TrainingDataExample_Cellpose2D.png',bbox_inches='tight',pad_inches=0)\n","\n","else:\n","\n"," f=plt.figure(figsize=(20,10))\n"," plt.subplot(1,3,1)\n"," plt.imshow(x, interpolation='nearest')\n"," plt.title('Training source')\n"," plt.axis('off');\n","\n"," plt.subplot(1,3,2)\n"," plt.imshow(x[:, :, int(Training_channel-1)],cmap='magma', interpolation='nearest')\n"," plt.title('Channel used for training')\n"," plt.axis('off');\n","\n"," plt.subplot(1,3,3)\n"," plt.imshow(y,cmap='nipy_spectral', interpolation='nearest')\n"," plt.title('Training target')\n"," plt.axis('off');\n","\n"," plt.savefig(base_path + '/TrainingDataExample_Cellpose2D.png',bbox_inches='tight',pad_inches=0)"]},{"cell_type":"markdown","metadata":{"id":"qEg6ar0PhuDY"},"source":["## **3.2. Data augmentation**\n","---\n",""]},{"cell_type":"markdown","metadata":{"id":"t6q9aqDUhxlw"},"source":["Data augmentation can improve training progress by amplifying differences in the dataset. This can be useful if the available dataset is small since, in this case, it is possible that a network could quickly learn every example in the dataset (overfitting), without augmentation. Augmentation is not necessary for training and if your training dataset is large you should disable it.\n","\n","By default, a x4 data augmentation is enabled in this notebook."]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"SblwpgmahfBl"},"outputs":[],"source":["#Data augmentation\n","\n","Use_Data_augmentation = False #@param {type:\"boolean\"}\n","\n","if Use_Data_augmentation:\n"," print(bcolors.WARNING+\"Data augmentation enabled\")\n"," Multiply_dataset_by = 4\n","\n","\n","if not Use_Data_augmentation:\n"," print(bcolors.WARNING+\"Data augmentation disabled\")"]},{"cell_type":"markdown","metadata":{"id":"s2NC_-Tuc02W"},"source":["\n","## **3.3. Using weights from a pre-trained model as initial weights**\n","---\n"," Here, you can set the path to a pre-trained model from which the weights can be extracted and used as a starting point for this training session. **This pre-trained model needs to be a Cellpose model**.\n","\n"," You can also use the pretrained models already available in Cellpose:\n","\n","- The cytoplasm model in cellpose is trained on two-channel images, where the first channel is the channel to segment, and the second channel is an optional nuclear channel.\n","\n","- The cytoplasm2 model is an updated cytoplasm model trained with user-submitted images.\n","\n","- The nuclear model in cellpose is trained on two-channel images, where the first channel is the channel to segment, and the second channel is always set to an array of zeros.\n","\n"," This option allows you to perform training over multiple Colab runtimes or to do transfer learning using models trained outside of ZeroCostDL4Mic. **You do not need to run this section if you want to train a network from scratch**.\n"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":27,"status":"ok","timestamp":1744184288926,"user":{"displayName":"guillaume Jacquemet","userId":"03282992684407736974"},"user_tz":-180},"id":"sLdgQM6Rc7vp","outputId":"a8f51ab5-aca2-4c3c-b366-70d2359006ac"},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[31mNo pretrained network will be used.\n"]}],"source":["# @markdown ##Loading weights from a pre-trained network\n","\n","Use_pretrained_model = False #@param {type:\"boolean\"}\n","\n","Pretrained_model = \"Own_model\" #@param [\"Cytoplasm\",\"Cytoplasm2\", \"Cytoplasm3\", \"Nuclei\", \"LiveCell\", \"TissueNet\", \"Own_model\"]\n","\n","#@markdown ###If using your own model, please provide the path to the model (not the folder):\n","pretrained_model_path = \"/content/gdrive/Shareddrives/CDM-annotations-Johanna/Ana_model2\" #@param {type:\"string\"}\n","\n","# --------------------- Check if we load a previously trained model ------------------------\n","\n","if Use_pretrained_model == True :\n","\n"," if Pretrained_model == \"Own_model\":\n","\n"," model_to_load = pretrained_model_path\n","\n"," print('The model '+ str(model_to_load) + \"will be used as a starting point\")\n","\n"," if Pretrained_model == \"Cytoplasm\":\n"," model_to_load = \"cyto\"\n"," print('The model Cytoplasm will be used as a starting point')\n","\n"," if Pretrained_model == \"Cytoplasm2\":\n"," model_to_load = \"cyto2\"\n"," print('The model Cytoplasm2 (cyto2) will be used as a starting point')\n","\n"," if Pretrained_model == \"Cytoplasm3\":\n"," model_to_load = \"cyto3\"\n"," print('The model Cytoplasm3 (cyto3) will be used as a starting point')\n","\n"," if Pretrained_model == \"Nuclei\":\n"," model_to_load = \"nuclei\"\n"," print('The model nuclei will be used as a starting point')\n","\n"," if Pretrained_model == \"LiveCell\":\n"," model_to_load = \"livecell\"\n"," print('The model LiveCell will be used as a starting point')\n","\n"," if Pretrained_model == \"TissueNet\":\n"," model_to_load = \"tissuenet\"\n"," print('The model TissueNet will be used as a starting point')\n","\n","else:\n"," model_to_load = None\n"," print(bcolors.WARNING+'No pretrained network will be used.')\n","\n"]},{"cell_type":"markdown","metadata":{"id":"qeYZ7PeValfs"},"source":["# **4. Train the network**\n","---"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"tsn8WV3Wl0sG"},"outputs":[],"source":["#@markdown ##Create the model and dataset objects\n","\n","\n","# Here we check that the model destination folder is empty\n","if os.path.exists(model_path+'/'+model_name):\n"," print(bcolors.WARNING +\"!! WARNING: Model folder already exists and has been removed !!\" + W)\n"," shutil.rmtree(model_path+'/'+model_name)\n","\n","os.makedirs(model_path+\"/\"+model_name)\n","\n","\n","#To use cellpose to work we need to organise the data in a way the network can understand\n","\n","# Here we count the number of files in the training target folder\n","Filelist = os.listdir(Training_target)\n","number_files = len(Filelist)\n","\n","# Here we count the number of file to use for validation\n","Image_for_validation = int((number_files)*(percentage_validation/100))\n","\n","if Image_for_validation == 0:\n"," Image_for_validation = 1\n","\n","Saving_path = base_path + \"/\" + model_name\n","\n","if os.path.exists(Saving_path):\n"," shutil.rmtree(Saving_path)\n","os.makedirs(Saving_path)\n","\n","train_folder = Saving_path+\"/train_folder\"\n","os.makedirs(train_folder)\n","\n","test_folder = Saving_path+\"/test_folder\"\n","os.makedirs(test_folder)\n","\n","index = 0\n","\n","print('Copying training source data...')\n","for f in tqdm(os.listdir(Training_source)):\n"," short_name = os.path.splitext(f)\n","\n"," if index < Image_for_validation:\n"," shutil.copyfile(Training_source+\"/\"+f, test_folder+\"/\"+short_name[0]+\"_img.tif\")\n"," shutil.copyfile(Training_target+\"/\"+f, test_folder+\"/\"+short_name[0]+\"_masks.tif\")\n"," else:\n"," shutil.copyfile(Training_source+\"/\"+f, train_folder+\"/\"+short_name[0]+\"_img.tif\")\n"," shutil.copyfile(Training_target+\"/\"+f, train_folder+\"/\"+short_name[0]+\"_masks.tif\")\n"," index = index +1\n","\n","print(\"Done\")\n","\n","pdf_export(augmentation = Use_Data_augmentation, pretrained_model = Use_pretrained_model)\n","\n"]},{"cell_type":"markdown","metadata":{"id":"jRorFe296LgI"},"source":["## **4.2. Start Training**\n","---\n","\n","When playing the cell below you should see updates after each epoch (round). Network training can take some time.\n","\n","* **CRITICAL NOTE:** Training is currently done using Torch.\n","\n","* **CRITICAL NOTE:** Google Colab has a time limit for processing (to prevent using GPU power for datamining). Training time must be less than 12 hours! If training takes longer than 12 hours, please decrease the number of epochs or number of patches. Another way circumvent this is to save the parameters of the model after training and start training again from this point.\n"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"YXUnd3awi6K3"},"outputs":[],"source":["#@markdown ##Start training\n","\n","start = time.time()\n","\n","if not Use_Data_augmentation:\n"," !python -m cellpose --train --use_gpu --dir \"$train_folder\" --test_dir \"$test_folder\" --pretrained_model $model_to_load --chan $Training_channel --chan2 $Second_training_channel --n_epochs $number_of_epochs --learning_rate $initial_learning_rate --batch_size $batch_size --img_filter img --mask_filter masks --verbose\n","\n","else:\n"," !python -m cellpose --train --use_gpu --dir \"$train_folder\" --test_dir \"$test_folder\" --pretrained_model $model_to_load --chan $Training_channel --chan2 $Second_training_channel --n_epochs $number_of_epochs --learning_rate $initial_learning_rate --batch_size $batch_size --img_filter img --mask_filter masks --verbose\n","\n","\n","\n","\n","\n","#Here we copy the model to the result folder after training\n","\n","if os.path.exists(model_path+'/'+model_name):\n"," print(bcolors.WARNING +\"!! WARNING: Model folder already exists and has been removed !!\" + W)\n"," shutil.rmtree(model_path+'/'+model_name)\n","destination = shutil.copytree(Saving_path+\"/train_folder/models\", model_path+\"/\"+model_name)\n","\n","# Displaying the time elapsed for training\n","dt = time.time() - start\n","mins, sec = divmod(dt, 60)\n","hour, mins = divmod(mins, 60)\n","print(\"Time elapsed:\",hour, \"hour(s)\",mins,\"min(s)\",round(sec),\"sec(s)\")\n","print(\"Your model is also available here: \"+str(model_path+\"/\"+model_name))\n","\n","\n","pdf_export(trained=True, augmentation = Use_Data_augmentation, pretrained_model = Use_pretrained_model)\n","\n"]},{"cell_type":"markdown","metadata":{"id":"qvbm9EJGaXr9"},"source":["# **5. Evaluate your model**\n","---\n","\n","This section allows the user to perform important quality checks on the validity and generalisability of the trained model. \n","\n","\n","**We highly recommend to perform quality control on all newly trained models.**\n","\n","**`model_choice`:** Choose the model to use to make predictions. This model needs to be a Cellpose model. You can also use the pretrained models already available in cellpose:\n","\n","- The cytoplasm model in cellpose is trained on two-channel images, where the first channel is the channel to segment, and the second channel is an optional nuclear channel.\n","- The cytoplasm2 and 3 models are updated cytoplasm model trained with user-submitted images.\n","\n","- The Nuclei model in cellpose is trained on two-channel images, where the first channel is the channel to segment, and the second channel is always set to an array of zeros.\n","\n","- The 'livecell' model was trained on all training images from the [livecell dataset](https://sartorius-research.github.io/LIVECell/). These images only have a cytoplasm channel.\n","\n","- The 'tissuenet' model was trained on all training images from the [tissuenet dataset](https://datasets.deepcell.org/). These images have a cytoplasm channel and a nuclear channel\n","\n","- The bacteria Omnipose model is an Omnipose model dedicated to the segmentation of bacteria or other elongated cells.\n","\n","- The cytoplasm2 Omnipose model is an Omnipose model trained using the same dataset as the cellpose cytoplasm2 model.\n","\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"IeiU6D2jGDh4"},"outputs":[],"source":["# model name and path\n","#@markdown ###Do you want to assess the model you just trained ?\n","Use_the_current_trained_model = True #@param {type:\"boolean\"}\n","\n","#@markdown ###If not, indicate which model you want to assess:\n","\n","QC_model = \"Own_model\" #@param [\"Cytoplasm\",\"Cytoplasm2\", \"Cytoplasm3\", \"LiveCell\", \"TissueNet\", \"Cytoplasm2_Omnipose\", \"Bacteria_Omnipose\", \"Nuclei\", \"Own_model\"]\n","\n","#@markdown ###If using your own model, please provide the path to the model (not the folder):\n","\n","QC_model_path = \"\" #@param {type:\"string\"}\n","\n","#@markdown ###If using the Cellpose or Omnipose models, please indicate where you want to save the results:\n","Saving_path = \"\" #@param {type:\"string\"}\n","\n","\n","if Use_the_current_trained_model :\n","\n"," list_files = sorted([i for i in os.listdir(model_path+\"/\"+model_name) if not i.endswith('.pdf')])\n","\n"," QC_model_path = model_path+\"/\"+model_name+\"/\"+list_files[0]\n"," QC_model = \"Own_model\"\n","\n"," #model = models.CellposeModel(gpu=True, pretrained_model=QC_model_path, torch=False, diam_mean=30.0, net_avg=True, device=None, residual_on=True, style_on=True, concatenation=False)\n"," #model = models.CellposeModel(gpu=True, pretrained_model=QC_model_path, diam_mean=30.0, net_avg=True, device=None, residual_on=True, style_on=True, concatenation=False)\n"," model = models.CellposeModel(gpu=True, pretrained_model=QC_model_path)\n","\n"," QC_model_folder = os.path.dirname(QC_model_path)\n"," QC_model_name = os.path.basename(QC_model_folder)\n"," Saving_path = QC_model_folder\n","\n"," print(\"The \"+str(QC_model_name)+\" model will be evaluated\")\n","\n","if not Use_the_current_trained_model:\n","\n"," if QC_model == \"Cytoplasm\":\n"," model = models.Cellpose(gpu=True, model_type=\"cyto\")\n"," QC_model_folder = Saving_path\n"," QC_model_name = \"Cytoplasm\"\n","\n"," print('The model \"Cytoplasm\" will be evaluated')\n","\n"," if QC_model == \"Cytoplasm2\":\n"," model = models.Cellpose(gpu=True, model_type=\"cyto2\")\n"," QC_model_folder = Saving_path\n"," QC_model_name = \"Cytoplasm2\"\n","\n"," print('The model \"Cytoplasm2\" will be evaluated')\n","\n"," if model_choice == \"Cytoplasm3\":\n"," channels=[segment_channel,nuclear_channel]\n"," model = models.Cellpose(gpu=True, model_type=\"cyto3\")\n"," print(\"Cytoplasm 3 model enabled\")\n","\n"," if QC_model == \"Cytoplasm2_Omnipose\":\n"," model = models.Cellpose(gpu=True, model_type=\"cyto2_omni\")\n"," QC_model_folder = Saving_path\n"," QC_model_name = \"Cytoplasm2_Omnipose\"\n"," print(\"The model Cytoplasm2_Omnipose will be evaluated\")\n","\n"," if QC_model == \"LiveCell\":\n"," channels=[segment_channel,0]\n"," model = models.CellposeModel(gpu=True, model_type=\"livecell\")\n"," print(\"LiveCell model enabled\")\n","\n"," if QC_model == \"Bacteria_Omnipose\":\n"," channels=[segment_channel,nuclear_channel]\n"," model = models.Cellpose(gpu=True, model_type=\"bact_omni\")\n"," Object_diameter = 0\n"," print(\"Bacteria_omnipose model enabled\")\n","\n"," if QC_model == \"TissueNet\":\n"," channels=[segment_channel,nuclear_channel]\n"," model = models.CellposeModel(gpu=True, model_type='tissuenet')\n"," print(\"TissueNet model enabled\")\n","\n"," if QC_model == \"Nuclei\":\n"," model = models.Cellpose(gpu=True, model_type=\"nuclei\")\n","\n"," QC_model_folder = Saving_path\n"," QC_model_name = \"Nuclei\"\n","\n"," print('The model \"Nuclei\" will be evaluated')\n","\n"," if QC_model == \"Own_model\":\n","\n"," if os.path.exists(QC_model_path):\n"," model = models.CellposeModel(gpu=True, pretrained_model=QC_model_path, torch=True, diam_mean=30.0, net_avg=True, device=None, residual_on=True, style_on=True, concatenation=False)\n","\n"," QC_model_folder = os.path.dirname(QC_model_path)\n"," Saving_path = QC_model_folder\n"," QC_model_name = os.path.basename(QC_model_folder)\n"," print(\"The \"+str(QC_model_name)+\" model will be evaluated\")\n","\n"," else:\n"," print(bcolors.WARNING+'!! WARNING: The chosen model does not exist !!')\n"," print('Please make sure you provide a valid model path and model name before proceeding further.')\n","\n","#Here we make the folder to save the resuslts if it does not exists\n","\n","if not Saving_path == \"\":\n"," if os.path.exists(QC_model_folder) == False:\n"," os.makedirs(QC_model_folder)\n","else:\n"," print(bcolors.WARNING+'!! WARNING: Indicate where you want to save the results')\n","\n","\n","# Here we load the def that perform the QC, code taken from StarDist https://github.com/mpicbg-csbd/stardist/blob/master/stardist/matching.py\n","\n","import numpy as np\n","from numba import jit\n","from tqdm import tqdm\n","from scipy.optimize import linear_sum_assignment\n","from collections import namedtuple\n","\n","\n","matching_criteria = dict()\n","\n","def label_are_sequential(y):\n"," \"\"\" returns true if y has only sequential labels from 1... \"\"\"\n"," labels = np.unique(y)\n"," return (set(labels)-{0}) == set(range(1,1+labels.max()))\n","\n","\n","def is_array_of_integers(y):\n"," return isinstance(y,np.ndarray) and np.issubdtype(y.dtype, np.integer)\n","\n","\n","def _check_label_array(y, name=None, check_sequential=False):\n"," err = ValueError(\"{label} must be an array of {integers}.\".format(\n"," label = 'labels' if name is None else name,\n"," integers = ('sequential ' if check_sequential else '') + 'non-negative integers',\n"," ))\n"," is_array_of_integers(y) or print(\"An error occured\")\n"," if check_sequential:\n"," label_are_sequential(y) or print(\"An error occured\")\n"," else:\n"," y.min() >= 0 or print(\"An error occured\")\n"," return True\n","\n","\n","def label_overlap(x, y, check=True):\n"," if check:\n"," _check_label_array(x,'x',True)\n"," _check_label_array(y,'y',True)\n"," x.shape == y.shape or _raise(ValueError(\"x and y must have the same shape\"))\n"," return _label_overlap(x, y)\n","\n","@jit(nopython=True)\n","def _label_overlap(x, y):\n"," x = x.ravel()\n"," y = y.ravel()\n"," overlap = np.zeros((1+x.max(),1+y.max()), dtype=np.uint)\n"," for i in range(len(x)):\n"," overlap[x[i],y[i]] += 1\n"," return overlap\n","\n","\n","def intersection_over_union(overlap):\n"," _check_label_array(overlap,'overlap')\n"," if np.sum(overlap) == 0:\n"," return overlap\n"," n_pixels_pred = np.sum(overlap, axis=0, keepdims=True)\n"," n_pixels_true = np.sum(overlap, axis=1, keepdims=True)\n"," return overlap / (n_pixels_pred + n_pixels_true - overlap)\n","\n","matching_criteria['iou'] = intersection_over_union\n","\n","\n","def intersection_over_true(overlap):\n"," _check_label_array(overlap,'overlap')\n"," if np.sum(overlap) == 0:\n"," return overlap\n"," n_pixels_true = np.sum(overlap, axis=1, keepdims=True)\n"," return overlap / n_pixels_true\n","\n","matching_criteria['iot'] = intersection_over_true\n","\n","\n","def intersection_over_pred(overlap):\n"," _check_label_array(overlap,'overlap')\n"," if np.sum(overlap) == 0:\n"," return overlap\n"," n_pixels_pred = np.sum(overlap, axis=0, keepdims=True)\n"," return overlap / n_pixels_pred\n","\n","matching_criteria['iop'] = intersection_over_pred\n","\n","\n","def precision(tp,fp,fn):\n"," return tp/(tp+fp) if tp > 0 else 0\n","def recall(tp,fp,fn):\n"," return tp/(tp+fn) if tp > 0 else 0\n","def accuracy(tp,fp,fn):\n"," # also known as \"average precision\" (?)\n"," # -> https://www.kaggle.com/c/data-science-bowl-2018#evaluation\n"," return tp/(tp+fp+fn) if tp > 0 else 0\n","def f1(tp,fp,fn):\n"," # also known as \"dice coefficient\"\n"," return (2*tp)/(2*tp+fp+fn) if tp > 0 else 0\n","\n","\n","def _safe_divide(x,y):\n"," return x/y if y>0 else 0.0\n","\n","def matching(y_true, y_pred, thresh=0.5, criterion='iou', report_matches=False):\n"," \"\"\"Calculate detection/instance segmentation metrics between ground truth and predicted label images.\n"," Currently, the following metrics are implemented:\n"," 'fp', 'tp', 'fn', 'precision', 'recall', 'accuracy', 'f1', 'criterion', 'thresh', 'n_true', 'n_pred', 'mean_true_score', 'mean_matched_score', 'panoptic_quality'\n"," Corresponding objects of y_true and y_pred are counted as true positives (tp), false positives (fp), and false negatives (fn)\n"," whether their intersection over union (IoU) >= thresh (for criterion='iou', which can be changed)\n"," * mean_matched_score is the mean IoUs of matched true positives\n"," * mean_true_score is the mean IoUs of matched true positives but normalized by the total number of GT objects\n"," * panoptic_quality defined as in Eq. 1 of Kirillov et al. \"Panoptic Segmentation\", CVPR 2019\n"," Parameters\n"," ----------\n"," y_true: ndarray\n"," ground truth label image (integer valued)\n"," predicted label image (integer valued)\n"," thresh: float\n"," threshold for matching criterion (default 0.5)\n"," criterion: string\n"," matching criterion (default IoU)\n"," report_matches: bool\n"," if True, additionally calculate matched_pairs and matched_scores (note, that this returns even gt-pred pairs whose scores are below 'thresh')\n"," Returns\n"," -------\n"," Matching object with different metrics as attributes\n"," Examples\n"," --------\n"," >>> y_true = np.zeros((100,100), np.uint16)\n"," >>> y_true[10:20,10:20] = 1\n"," >>> y_pred = np.roll(y_true,5,axis = 0)\n"," >>> stats = matching(y_true, y_pred)\n"," >>> print(stats)\n"," Matching(criterion='iou', thresh=0.5, fp=1, tp=0, fn=1, precision=0, recall=0, accuracy=0, f1=0, n_true=1, n_pred=1, mean_true_score=0.0, mean_matched_score=0.0, panoptic_quality=0.0)\n"," \"\"\"\n"," _check_label_array(y_true,'y_true')\n"," _check_label_array(y_pred,'y_pred')\n"," y_true.shape == y_pred.shape or _raise(ValueError(\"y_true ({y_true.shape}) and y_pred ({y_pred.shape}) have different shapes\".format(y_true=y_true, y_pred=y_pred)))\n"," criterion in matching_criteria or _raise(ValueError(\"Matching criterion '%s' not supported.\" % criterion))\n"," if thresh is None: thresh = 0\n"," thresh = float(thresh) if np.isscalar(thresh) else map(float,thresh)\n","\n"," y_true, _, map_rev_true = relabel_sequential(y_true)\n"," y_pred, _, map_rev_pred = relabel_sequential(y_pred)\n","\n"," overlap = label_overlap(y_true, y_pred, check=False)\n"," scores = matching_criteria[criterion](overlap)\n"," assert 0 <= np.min(scores) <= np.max(scores) <= 1\n","\n"," # ignoring background\n"," scores = scores[1:,1:]\n"," n_true, n_pred = scores.shape\n"," n_matched = min(n_true, n_pred)\n","\n"," def _single(thr):\n"," not_trivial = n_matched > 0 and np.any(scores >= thr)\n"," if not_trivial:\n"," # compute optimal matching with scores as tie-breaker\n"," costs = -(scores >= thr).astype(float) - scores / (2*n_matched)\n"," true_ind, pred_ind = linear_sum_assignment(costs)\n"," assert n_matched == len(true_ind) == len(pred_ind)\n"," match_ok = scores[true_ind,pred_ind] >= thr\n"," tp = np.count_nonzero(match_ok)\n"," else:\n"," tp = 0\n"," fp = n_pred - tp\n"," fn = n_true - tp\n"," # assert tp+fp == n_pred\n"," # assert tp+fn == n_true\n","\n"," # the score sum over all matched objects (tp)\n"," sum_matched_score = np.sum(scores[true_ind,pred_ind][match_ok]) if not_trivial else 0.0\n","\n"," # the score average over all matched objects (tp)\n"," mean_matched_score = _safe_divide(sum_matched_score, tp)\n"," # the score average over all gt/true objects\n"," mean_true_score = _safe_divide(sum_matched_score, n_true)\n"," panoptic_quality = _safe_divide(sum_matched_score, tp+fp/2+fn/2)\n","\n"," stats_dict = dict (\n"," criterion = criterion,\n"," thresh = thr,\n"," fp = fp,\n"," tp = tp,\n"," fn = fn,\n"," precision = precision(tp,fp,fn),\n"," recall = recall(tp,fp,fn),\n"," accuracy = accuracy(tp,fp,fn),\n"," f1 = f1(tp,fp,fn),\n"," n_true = n_true,\n"," n_pred = n_pred,\n"," mean_true_score = mean_true_score,\n"," mean_matched_score = mean_matched_score,\n"," panoptic_quality = panoptic_quality,\n"," )\n"," if bool(report_matches):\n"," if not_trivial:\n"," stats_dict.update (\n"," # int() to be json serializable\n"," matched_pairs = tuple((int(map_rev_true[i]),int(map_rev_pred[j])) for i,j in zip(1+true_ind,1+pred_ind)),\n"," matched_scores = tuple(scores[true_ind,pred_ind]),\n"," matched_tps = tuple(map(int,np.flatnonzero(match_ok))),\n"," )\n"," else:\n"," stats_dict.update (\n"," matched_pairs = (),\n"," matched_scores = (),\n"," matched_tps = (),\n"," )\n"," return namedtuple('Matching',stats_dict.keys())(*stats_dict.values())\n","\n"," return _single(thresh) if np.isscalar(thresh) else tuple(map(_single,thresh))\n","\n","\n","\n","def matching_dataset(y_true, y_pred, thresh=0.5, criterion='iou', by_image=False, show_progress=True, parallel=False):\n"," \"\"\"matching metrics for list of images, see `stardist.matching.matching`\n"," \"\"\"\n"," len(y_true) == len(y_pred) or _raise(ValueError(\"y_true and y_pred must have the same length.\"))\n"," return matching_dataset_lazy (\n"," tuple(zip(y_true,y_pred)), thresh=thresh, criterion=criterion, by_image=by_image, show_progress=show_progress, parallel=parallel,\n"," )\n","\n","\n","\n","def matching_dataset_lazy(y_gen, thresh=0.5, criterion='iou', by_image=False, show_progress=True, parallel=False):\n","\n"," expected_keys = set(('fp', 'tp', 'fn', 'precision', 'recall', 'accuracy', 'f1', 'criterion', 'thresh', 'n_true', 'n_pred', 'mean_true_score', 'mean_matched_score', 'panoptic_quality'))\n","\n"," single_thresh = False\n"," if np.isscalar(thresh):\n"," single_thresh = True\n"," thresh = (thresh,)\n","\n"," tqdm_kwargs = {}\n"," tqdm_kwargs['disable'] = not bool(show_progress)\n"," if int(show_progress) > 1:\n"," tqdm_kwargs['total'] = int(show_progress)\n","\n"," # compute matching stats for every pair of label images\n"," if parallel:\n"," from concurrent.futures import ThreadPoolExecutor\n"," fn = lambda pair: matching(*pair, thresh=thresh, criterion=criterion, report_matches=False)\n"," with ThreadPoolExecutor() as pool:\n"," stats_all = tuple(pool.map(fn, tqdm(y_gen,**tqdm_kwargs)))\n"," else:\n"," stats_all = tuple (\n"," matching(y_t, y_p, thresh=thresh, criterion=criterion, report_matches=False)\n"," for y_t,y_p in tqdm(y_gen,**tqdm_kwargs)\n"," )\n","\n"," # accumulate results over all images for each threshold separately\n"," n_images, n_threshs = len(stats_all), len(thresh)\n"," accumulate = [{} for _ in range(n_threshs)]\n"," for stats in stats_all:\n"," for i,s in enumerate(stats):\n"," acc = accumulate[i]\n"," for k,v in s._asdict().items():\n"," if k == 'mean_true_score' and not bool(by_image):\n"," # convert mean_true_score to \"sum_matched_score\"\n"," acc[k] = acc.setdefault(k,0) + v * s.n_true\n"," else:\n"," try:\n"," acc[k] = acc.setdefault(k,0) + v\n"," except TypeError:\n"," pass\n","\n"," # normalize/compute 'precision', 'recall', 'accuracy', 'f1'\n"," for thr,acc in zip(thresh,accumulate):\n"," set(acc.keys()) == expected_keys or _raise(ValueError(\"unexpected keys\"))\n"," acc['criterion'] = criterion\n"," acc['thresh'] = thr\n"," acc['by_image'] = bool(by_image)\n"," if bool(by_image):\n"," for k in ('precision', 'recall', 'accuracy', 'f1', 'mean_true_score', 'mean_matched_score', 'panoptic_quality'):\n"," acc[k] /= n_images\n"," else:\n"," tp, fp, fn, n_true = acc['tp'], acc['fp'], acc['fn'], acc['n_true']\n"," sum_matched_score = acc['mean_true_score']\n","\n"," mean_matched_score = _safe_divide(sum_matched_score, tp)\n"," mean_true_score = _safe_divide(sum_matched_score, n_true)\n"," panoptic_quality = _safe_divide(sum_matched_score, tp+fp/2+fn/2)\n","\n"," acc.update(\n"," precision = precision(tp,fp,fn),\n"," recall = recall(tp,fp,fn),\n"," accuracy = accuracy(tp,fp,fn),\n"," f1 = f1(tp,fp,fn),\n"," mean_true_score = mean_true_score,\n"," mean_matched_score = mean_matched_score,\n"," panoptic_quality = panoptic_quality,\n"," )\n","\n"," accumulate = tuple(namedtuple('DatasetMatching',acc.keys())(*acc.values()) for acc in accumulate)\n"," return accumulate[0] if single_thresh else accumulate\n","\n","\n","\n","# copied from scikit-image master for now (remove when part of a release)\n","def relabel_sequential(label_field, offset=1):\n"," \"\"\"Relabel arbitrary labels to {`offset`, ... `offset` + number_of_labels}.\n"," This function also returns the forward map (mapping the original labels to\n"," the reduced labels) and the inverse map (mapping the reduced labels back\n"," to the original ones).\n"," Parameters\n"," ----------\n"," label_field : numpy array of int, arbitrary shape\n"," An array of labels, which must be non-negative integers.\n"," offset : int, optional\n"," The return labels will start at `offset`, which should be\n"," strictly positive.\n"," Returns\n"," -------\n"," relabeled : numpy array of int, same shape as `label_field`\n"," The input label field with labels mapped to\n"," {offset, ..., number_of_labels + offset - 1}.\n"," The data type will be the same as `label_field`, except when\n"," offset + number_of_labels causes overflow of the current data type.\n"," forward_map : numpy array of int, shape ``(label_field.max() + 1,)``\n"," The map from the original label space to the returned label\n"," space. Can be used to re-apply the same mapping. See examples\n"," for usage. The data type will be the same as `relabeled`.\n"," inverse_map : 1D numpy array of int, of length offset + number of labels\n"," The map from the new label space to the original space. This\n"," can be used to reconstruct the original label field from the\n"," relabeled one. The data type will be the same as `relabeled`.\n"," Notes\n"," -----\n"," The label 0 is assumed to denote the background and is never remapped.\n"," The forward map can be extremely big for some inputs, since its\n"," length is given by the maximum of the label field. However, in most\n"," situations, ``label_field.max()`` is much smaller than\n"," ``label_field.size``, and in these cases the forward map is\n"," guaranteed to be smaller than either the input or output images.\n"," Examples\n"," --------\n"," >>> from skimage.segmentation import relabel_sequential\n"," >>> label_field = np.array([1, 1, 5, 5, 8, 99, 42])\n"," >>> relab, fw, inv = relabel_sequential(label_field)\n"," >>> relab\n"," array([1, 1, 2, 2, 3, 5, 4])\n"," >>> fw\n"," array([0, 1, 0, 0, 0, 2, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n"," 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0,\n"," 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n"," 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n"," 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5])\n"," >>> inv\n"," array([ 0, 1, 5, 8, 42, 99])\n"," >>> (fw[label_field] == relab).all()\n"," True\n"," >>> (inv[relab] == label_field).all()\n"," True\n"," >>> relab, fw, inv = relabel_sequential(label_field, offset=5)\n"," >>> relab\n"," array([5, 5, 6, 6, 7, 9, 8])\n"," \"\"\"\n"," offset = int(offset)\n"," if offset <= 0:\n"," raise ValueError(\"Offset must be strictly positive.\")\n"," if np.min(label_field) < 0:\n"," raise ValueError(\"Cannot relabel array that contains negative values.\")\n"," max_label = int(label_field.max()) # Ensure max_label is an integer\n"," if not np.issubdtype(label_field.dtype, np.integer):\n"," new_type = np.min_scalar_type(max_label)\n"," label_field = label_field.astype(new_type)\n"," labels = np.unique(label_field)\n"," labels0 = labels[labels != 0]\n"," new_max_label = offset - 1 + len(labels0)\n"," new_labels0 = np.arange(offset, new_max_label + 1)\n"," output_type = label_field.dtype\n"," required_type = np.min_scalar_type(new_max_label)\n"," if np.dtype(required_type).itemsize > np.dtype(label_field.dtype).itemsize:\n"," output_type = required_type\n"," forward_map = np.zeros(max_label + 1, dtype=output_type)\n"," forward_map[labels0] = new_labels0\n"," inverse_map = np.zeros(new_max_label + 1, dtype=output_type)\n"," inverse_map[offset:] = labels0\n"," relabeled = forward_map[label_field]\n"," return relabeled, forward_map, inverse_map\n","\n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"Tbv6DpxZjVN3"},"source":["## **5.1. Inspection of the loss function**\n","---\n","\n","First, it is good practice to evaluate the training progress by comparing the training loss with the validation loss. The latter is a metric which shows how well the network performs on a subset of unseen data which is set aside from the training dataset. For more information on this, see for example [this review](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6381354/) by Nichols *et al.*\n","\n","**Training loss** describes an error value after each epoch for the difference between the model's prediction and its ground-truth target.\n","\n","**Validation loss** describes the same error value between the model's prediction on a validation image and compared to it's target.\n","\n","During training both values should decrease before reaching a minimal value which does not decrease further even after more training. Comparing the development of the validation loss with the training loss can give insights into the model's performance.\n","\n","Decreasing **Training loss** and **Validation loss** indicates that training is still necessary and increasing the `number_of_epochs` is recommended. Note that the curves can look flat towards the right side, just because of the y-axis scaling. The network has reached convergence once the curves flatten out. After this point no further training is required. If the **Validation loss** suddenly increases again an the **Training loss** simultaneously goes towards zero, it means that the network is overfitting to the training data. In other words the network is remembering the exact patterns from the training data and no longer generalizes well to unseen data. In this case the training dataset has to be increased.\n","\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"jtSv-B0AjX8j"},"outputs":[],"source":["#@markdown ###Not implemented yet"]},{"cell_type":"markdown","metadata":{"id":"s2VXDuiOF7r4"},"source":["## **5.2. Error mapping and quality metrics estimation**\n","---\n","This section will calculate the Intersection over Union score for all the images provided in the Source_QC_folder and Target_QC_folder ! The result for one of the image will also be displayed.\n","\n","The **Intersection over Union** (IuO) metric is a method that can be used to quantify the percent overlap between the target mask and your prediction output. **Therefore, the closer to 1, the better the performance.** This metric can be used to assess the quality of your model to accurately predict nuclei.\n","\n","Here, the IuO is both calculated over the whole image and on a per-object basis. The value displayed below is the IuO value calculated over the entire image. The IuO value calculated on a per-object basis is used to calculate the other metrics displayed.\n","\n","“n_true” refers to the number of objects present in the ground truth image. “n_pred” refers to the number of objects present in the predicted image.\n","\n","When a segmented object has an IuO value above 0.5 (compared to the corresponding ground truth), it is then considered a true positive. The number of “**true positives**” is available in the table below. The number of “false positive” is then defined as “**false positive**” = “n_pred” - “true positive”. The number of “false negative” is defined as “false negative” = “n_true” - “true positive”.\n","\n","The mean_matched_score is the mean IoUs of matched true positives. The mean_true_score is the mean IoUs of matched true positives but normalized by the total number of ground truth objects. The panoptic_quality is calculated as described by [Kirillov et al. 2019](https://arxiv.org/abs/1801.00868).\n","\n","For more information about the other metric displayed, please consult the SI of the paper describing ZeroCostDL4Mic.\n","\n"," The results can be found in the \"*Quality Control*\" folder which is located inside your \"model_folder\".\n","\n","**`Channel_to_segment`:** Choose the channel to segment. If using single-channel grayscale images, choose \"Grayscale\".\n","\n","**`Nuclear_channel`:** If you are using a model that segment the \"cytoplasm\", you can use a nuclear channel to aid the segmentation.\n","\n","**`Object_diameter`:** Indicate the diameter of the objects (cells or Nuclei) you want to segment (in pixel). If you input \"0\", this parameter will be estimated automatically for each of your images.\n","\n","**`Flow_threshold`:** This parameter controls the maximum allowed error of the flows for each mask. Increase this threshold if cellpose is not returning as many masks as you'd expect. Similarly, decrease this threshold if cellpose is returning too many ill-shaped masks. Set to 0.0 to turn off so no cells are discarded. **Default value: 0.4**\n","\n","**`mask_threshold`:** The pixels greater than the mask_threshold are used to run dynamics and determine masks. Decrease this threshold if cellpose is not returning as many masks as you'd expect. Similarly, increase this threshold if cellpose is returning too many masks, particularly from dim areas. **Default value: 0.0**"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"BUrTuonhEH5J"},"outputs":[],"source":["#@markdown ##Choose the folders that contain your Quality Control dataset\n","\n","Source_QC_folder = \"\" #@param{type:\"string\"}\n","Target_QC_folder = \"\" #@param{type:\"string\"}\n","\n","Channel_to_segment= \"Red\" #@param [\"Grayscale\", \"Blue\", \"Green\", \"Red\"]\n","\n","# @markdown ###If you chose the model \"cytoplasm\" indicate if you also have a nuclear channel that can be used to aid the segmentation.\n","\n","Nuclear_channel= \"None\" #@param [\"None\", \"Blue\", \"Green\", \"Red\"]\n","\n","#@markdown ### Segmentation parameters:\n","Object_diameter = 0#@param {type:\"number\"}\n","\n","Flow_threshold = 0.4 #@param {type:\"slider\", min:0.0, max:3, step:0.1}\n","\n","mask_threshold=0 #@param {type:\"slider\", min:-6, max:6, step:1}\n","\n","if Object_diameter is 0:\n"," Object_diameter = None\n"," print(\"The cell size will be estimated automatically for each image\")\n","\n","\n","# Find the number of channel in the input image\n","\n","random_choice = random.choice(os.listdir(Source_QC_folder))\n","x = io.imread(Source_QC_folder+\"/\"+random_choice)\n","n_channel = 1 if x.ndim == 2 else x.shape[-1]\n","\n","if Channel_to_segment == \"Grayscale\":\n"," segment_channel = 0\n","\n"," if not n_channel == 1:\n"," print(bcolors.WARNING +\"!! WARNING: your image has more than one channel, choose which channel you want to use for QC !!\")\n","\n","if Channel_to_segment == \"Blue\":\n"," segment_channel = 3\n","\n","if Channel_to_segment == \"Green\":\n"," segment_channel = 2\n","\n","if Channel_to_segment == \"Red\":\n"," segment_channel = 1\n","\n","if Nuclear_channel == \"Blue\":\n"," nuclear_channel = 3\n","\n","if Nuclear_channel == \"Green\":\n"," nuclear_channel = 2\n","\n","if Nuclear_channel == \"Red\":\n"," nuclear_channel = 1\n","\n","if Nuclear_channel == \"None\":\n"," nuclear_channel = 0\n","\n","if QC_model == \"Cytoplasm\":\n"," channels=[segment_channel,nuclear_channel]\n","\n","if QC_model == \"Cytoplasm2_Omnipose\":\n"," channels=[segment_channel,nuclear_channel]\n","\n","if QC_model == \"Bacteria_Omnipose\":\n"," channels=[segment_channel,nuclear_channel]\n","\n","if QC_model == \"Cytoplasm2\":\n"," channels=[segment_channel,nuclear_channel]\n","\n","if QC_model == \"Nuclei\":\n"," channels=[segment_channel,0]\n","\n","if QC_model == \"Own_model\":\n"," channels=[segment_channel,nuclear_channel]\n","\n","#Create a quality control Folder and check if the folder already exist\n","if os.path.exists(QC_model_folder+\"/Quality Control\") == False:\n"," os.makedirs(QC_model_folder+\"/Quality Control\")\n","\n","if os.path.exists(QC_model_folder+\"/Quality Control/Prediction\"):\n"," shutil.rmtree(QC_model_folder+\"/Quality Control/Prediction\")\n","os.makedirs(QC_model_folder+\"/Quality Control/Prediction\")\n","\n","\n","# Here we need to make predictions\n","\n","for name in os.listdir(Source_QC_folder):\n","\n"," print(\"Performing prediction on: \"+name)\n"," image = io.imread(Source_QC_folder+\"/\"+name)\n","\n"," short_name = os.path.splitext(name)\n","\n"," if QC_model == \"Own_model\" or QC_model == \"LiveCell\" or QC_model == \"TissueNet\":\n"," masks, flows, styles = model.eval(image, diameter=Object_diameter, flow_threshold=Flow_threshold,cellprob_threshold=mask_threshold, channels=channels)\n"," else:\n"," masks, flows, styles, diams = model.eval(image, diameter=Object_diameter, flow_threshold=Flow_threshold,cellprob_threshold=mask_threshold, channels=channels)\n","\n"," os.chdir(QC_model_folder+\"/Quality Control/Prediction\")\n"," imsave(str(short_name[0])+\".tif\", masks)\n","\n","# Here we start testing the differences between GT and predicted masks\n","\n","with open(QC_model_folder+\"/Quality Control/Quality_Control for \"+QC_model_name+\".csv\", \"w\", newline='') as file:\n"," writer = csv.writer(file, delimiter=\",\")\n"," writer.writerow([\"image\",\"Prediction v. GT Intersection over Union\", \"false positive\", \"true positive\", \"false negative\", \"precision\", \"recall\", \"accuracy\", \"f1 score\", \"n_true\", \"n_pred\", \"mean_true_score\", \"mean_matched_score\", \"panoptic_quality\"])\n","\n","# define the images\n","\n"," for n in os.listdir(Source_QC_folder):\n","\n"," if not os.path.isdir(os.path.join(Source_QC_folder,n)):\n"," print('Running QC on: '+n)\n"," test_input = io.imread(os.path.join(Source_QC_folder,n))\n"," test_prediction = io.imread(os.path.join(QC_model_folder+\"/Quality Control/Prediction\",n))\n"," test_ground_truth_image = io.imread(os.path.join(Target_QC_folder, n))\n","\n"," # Calculate the matching (with IoU threshold `thresh`) and all metrics\n","\n"," stats = matching(test_ground_truth_image, test_prediction, thresh=0.5)\n","\n","\n"," #Convert pixel values to 0 or 255\n"," test_prediction_0_to_255 = test_prediction\n"," test_prediction_0_to_255[test_prediction_0_to_255>0] = 255\n","\n"," #Convert pixel values to 0 or 255\n"," test_ground_truth_0_to_255 = test_ground_truth_image\n"," test_ground_truth_0_to_255[test_ground_truth_0_to_255>0] = 255\n","\n","\n"," # Intersection over Union metric\n","\n"," intersection = np.logical_and(test_ground_truth_0_to_255, test_prediction_0_to_255)\n"," union = np.logical_or(test_ground_truth_0_to_255, test_prediction_0_to_255)\n"," iou_score = np.sum(intersection) / np.sum(union)\n"," writer.writerow([n, str(iou_score), str(stats.fp), str(stats.tp), str(stats.fn), str(stats.precision), str(stats.recall), str(stats.accuracy), str(stats.f1), str(stats.n_true), str(stats.n_pred), str(stats.mean_true_score), str(stats.mean_matched_score), str(stats.panoptic_quality)])\n","\n","from tabulate import tabulate\n","\n","df = pd.read_csv (QC_model_folder+\"/Quality Control/Quality_Control for \"+QC_model_name+\".csv\")\n","print(tabulate(df, headers='keys', tablefmt='psql'))\n","\n","\n","from astropy.visualization import simple_norm\n","\n","# ------------- For display ------------\n","print('--------------------------------------------------------------')\n","@interact\n","def show_QC_results(file = os.listdir(Source_QC_folder)):\n","\n","\n"," plt.figure(figsize=(25,5))\n"," if n_channel > 1:\n"," source_image = io.imread(os.path.join(Source_QC_folder, file))\n"," if n_channel == 1:\n"," source_image = io.imread(os.path.join(Source_QC_folder, file), as_gray = True)\n","\n"," target_image = io.imread(os.path.join(Target_QC_folder, file), as_gray = True)\n"," prediction = io.imread(QC_model_folder+\"/Quality Control/Prediction/\"+file, as_gray = True)\n","\n"," stats = matching(prediction, target_image, thresh=0.5)\n","\n"," target_image_mask = np.empty_like(target_image)\n"," target_image_mask[target_image > 0] = 255\n"," target_image_mask[target_image == 0] = 0\n","\n"," prediction_mask = np.empty_like(prediction)\n"," prediction_mask[prediction > 0] = 255\n"," prediction_mask[prediction == 0] = 0\n","\n"," intersection = np.logical_and(target_image_mask, prediction_mask)\n"," union = np.logical_or(target_image_mask, prediction_mask)\n"," iou_score = np.sum(intersection) / np.sum(union)\n","\n"," norm = simple_norm(source_image, percent = 99)\n","\n"," #Input\n"," plt.subplot(1,4,1)\n"," plt.axis('off')\n"," if n_channel > 1:\n"," plt.imshow(source_image)\n"," if n_channel == 1:\n"," plt.imshow(source_image, aspect='equal', norm=norm, cmap='magma', interpolation='nearest')\n"," plt.title('Input')\n","\n"," #Ground-truth\n"," plt.subplot(1,4,2)\n"," plt.axis('off')\n"," plt.imshow(target_image_mask, aspect='equal', cmap='Greens')\n"," plt.title('Ground Truth')\n","\n"," #Prediction\n"," plt.subplot(1,4,3)\n"," plt.axis('off')\n"," plt.imshow(prediction_mask, aspect='equal', cmap='Purples')\n"," plt.title('Prediction')\n","\n"," #Overlay\n"," plt.subplot(1,4,4)\n"," plt.axis('off')\n"," plt.imshow(target_image_mask, cmap='Greens')\n"," plt.imshow(prediction_mask, alpha=0.5, cmap='Purples')\n"," plt.title('Ground Truth and Prediction, Intersection over Union:'+str(round(iou_score,3 )));\n"," plt.savefig(QC_model_folder+'/Quality Control/QC_example_data.png',bbox_inches='tight',pad_inches=0)\n","\n","\n","full_QC_model_path = QC_model_folder+'/'\n","qc_pdf_export()\n","\n"]},{"cell_type":"markdown","metadata":{"id":"Io62PUMLagFS"},"source":["# **6. Using the trained model**\n","\n","---\n","\n","In this section the unseen data is processed using the trained model (in section 4). First, your unseen images are uploaded and prepared for prediction. After that your trained model from section 4 is activated and finally saved into your Google Drive."]},{"cell_type":"markdown","metadata":{"id":"E29LWfWpjkZU"},"source":["\n","\n","## **6.1 Generate prediction(s) from unseen dataset (2D and 2D + t data)**\n","---\n","\n","The current trained model (from section 4.3) can now be used to process images. If an older model needs to be used, please untick the **Use_the_current_trained_model** box and enter the model's name and path to use. Predicted output images are saved in your **Prediction_folder** folder as restored image stacks (ImageJ-compatible TIFF images).\n","\n","**`Data_folder`:** This folder should contain the images that you want to predict using the network that you will train.\n","\n","**`Result_folder`:** This folder will contain the predicted output.\n","\n","**`Data_type`:** Please indicate if the images you want to predict are single images or stacks\n","\n","**`model_choice`:** Choose the model to use to make predictions. This model needs to be a Cellpose model. You can also use the pretrained models already available in cellpose:\n","\n","- The cytoplasm model in cellpose is trained on two-channel images, where the first channel is the channel to segment, and the second channel is an optional nuclear channel.\n","\n","- The cytoplasm2 model is an updated cytoplasm model trained with user-submitted images.\n","\n","- The Nuclei model in cellpose is trained on two-channel images, where the first channel is the channel to segment, and the second channel is always set to an array of zeros.\n","\n","- The 'LiveCell' model was trained on all training images from the [livecell dataset](https://sartorius-research.github.io/LIVECell/). These images only have a cytoplasm channel.\n","\n","- The 'TissueNet' model was trained on all training images from the [tissuenet dataset](https://datasets.deepcell.org/). These images have a cytoplasm channel and a nuclear channel\n","\n","- The bacteria Omnipose model is an Omnipose model dedicated to the segmentation of bacteria or other elongated cells.\n","\n","- The cytoplasm2 Omnipose model is an Omnipose model trained using the same dataset as the cellpose cytoplasm2 model.\n","\n","**`Channel_to_segment`:** Choose the channel to segment. If using single-channel grayscale images, choose \"Grayscale\".\n","\n","**`Nuclear_channel`:** If you are using a model that segment the \"cytoplasm\", you can use a nuclear channel to aid the segmentation.\n","\n","**`Object_diameter`:** Indicate the diameter of the objects (cells or Nuclei) you want to segment (in pixel). If you input \"0\", this parameter will be estimated automatically for each of your images.\n","\n","**`Flow_threshold`:** This parameter controls the maximum allowed error of the flows for each mask. Increase this threshold if cellpose is not returning as many masks as you'd expect. Similarly, decrease this threshold if cellpose is returning too many ill-shaped masks. **Default value: 0.4**\n","\n","**`mask_threshold`:** The pixels greater than the mask_threshold are used to run dynamics and determine masks. Decrease this threshold if cellpose is not returning as many masks as you'd expect. Similarly, increase this threshold if cellpose is returning too many masks, particularly from dim areas. **Default value: 0.0**\n","\n","**IMPORTANT:** One example result will be displayed first so that you can assess the quality of the prediction and change your settings accordingly. Once the most suitable settings have been chosen, press on the yellow button \"process your images\".\n","\n","\n","\n","\n","\n","\n"]},{"cell_type":"code","execution_count":5,"metadata":{"cellView":"form","colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["f768803545d041638b66941d850b32cd","790d6e6e473a46e78ffbfc1f72a931ed","dc90ae8a475c46f982be4967a6a83cb1","02f07ea9cda84469b0baf05bb6db2c8a","c2166230f60e4a72b094a9b20d79c774","8d43f28e23ed45b08129ed41526b4ca1","f5f6be3714e4455bb60cb90716dfe132","ba02d67024864367b9758224d2dd0db5","915fb161c4be4a38bb79b978b052fb5a","4dbbaa86e35c4e62bff56c8ef611932c","c21b34855eb846b7849b2f3d9a6c9682","bc880185cd8c43949ffcfd9cde6da3ee","a27783dced594a85bc19d29c84f99ce9","6b775a4590bf43f09e9c43be6304477b"]},"executionInfo":{"elapsed":155614,"status":"ok","timestamp":1744793708570,"user":{"displayName":"guillaume Jacquemet","userId":"03282992684407736974"},"user_tz":-180},"id":"mfgvhMk2xid9","outputId":"f45e0423-0675-41ba-a65c-fc54962ada7d"},"outputs":[{"output_type":"stream","name":"stdout","text":["Own model enabled\n","Stacks (2D + t) are now beeing predicted\n","--------------------------------------------------------------\n"]},{"output_type":"display_data","data":{"text/plain":["interactive(children=(Dropdown(description='file', options=('C2-Slide1.sld - Capture 6 - Position 1.tif', 'C2-…"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f768803545d041638b66941d850b32cd"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["interactive(children=(Button(description='Run Interact', style=ButtonStyle()), Output()), _dom_classes=('widge…"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ba02d67024864367b9758224d2dd0db5"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["
\n"," 
batch_process_stack
def batch_process_stack()
/content/<ipython-input-5-baee4452ba30><no docstring>
"]},"metadata":{}}],"source":["\n","#@markdown ### Provide the path to your dataset and to the folder where the predictions are saved, then play the cell to predict outputs from your unseen images.\n","\n","Data_folder = \"/content/gdrive/Shareddrives/CDM-annotations-Johanna/Repeat3/siCTRL/Cellpose\" #@param {type:\"string\"}\n","Result_folder = \"/content/gdrive/Shareddrives/CDM-annotations-Johanna/Repeat3/siCTRL/Cellpose_labels_JP\" #@param {type:\"string\"}\n","\n","#@markdown ###Are your data single images or stacks?\n","\n","Data_type = \"Stacks (2D + t)\" #@param [\"Single_Images\", \"Stacks (2D + t)\"]\n","\n","#@markdown ###What model do you want to use?\n","\n","model_choice = \"Own_model\" #@param [\"Cytoplasm\",\"Cytoplasm2\",\"Cytoplasm3\",\"LiveCell\", \"TissueNet\", \"Cytoplasm2_Omnipose\", \"Bacteria_Omnipose\", \"Nuclei\", \"Own_model\"]\n","\n","#@markdown ####If using your own model, please provide the path to the model (not the folder):\n","\n","Prediction_model = \"/content/gdrive/Shareddrives/CDM-annotations-Johanna/Ana_model_3\" #@param {type:\"string\"}\n","\n","#@markdown ### What channel do you want to segment?\n","\n","Channel_to_segment= \"Grayscale\" #@param [\"Grayscale\", \"Blue\", \"Green\", \"Red\"]\n","\n","# @markdown ###If you chose the model \"cytoplasm\" indicate if you also have a nuclear channel that can be used to aid the segmentation.\n","\n","Nuclear_channel= \"None\" #@param [\"None\", \"Blue\", \"Green\", \"Red\"]\n","\n","#@markdown ### Segmentation parameters:\n","Object_diameter = 340#@param {type:\"number\"}\n","\n","Flow_threshold = 0.4 #@param {type:\"slider\", min:0.0, max:3, step:0.1}\n","\n","mask_threshold=0 #@param {type:\"slider\", min:-6, max:6, step:1}\n","\n","# Find the number of channel in the input image\n","\n","random_choice = random.choice(os.listdir(Data_folder))\n","x = io.imread(Data_folder+\"/\"+random_choice)\n","n_channel = 1 if x.ndim == 2 else x.shape[-1]\n","\n","if Channel_to_segment == \"Grayscale\":\n"," segment_channel = 0\n","\n"," if Data_type == \"Single_Images\":\n"," if not n_channel == 1:\n"," print(bcolors.WARNING +\"!! WARNING: your image has more than one channel, choose which channel you want to use for your predictions !!\")\n","\n","if Channel_to_segment == \"Blue\":\n"," segment_channel = 3\n","\n","if Channel_to_segment == \"Green\":\n"," segment_channel = 2\n","\n","if Channel_to_segment == \"Red\":\n"," segment_channel = 1\n","\n","if Nuclear_channel == \"Blue\":\n"," nuclear_channel = 3\n","\n","if Nuclear_channel == \"Green\":\n"," nuclear_channel = 2\n","\n","if Nuclear_channel == \"Red\":\n"," nuclear_channel = 1\n","\n","if Nuclear_channel == \"None\":\n"," nuclear_channel = 0\n","\n","if model_choice == \"Cytoplasm\":\n"," channels=[segment_channel,nuclear_channel]\n"," model = models.Cellpose(gpu=True, model_type=\"cyto\")\n"," print(\"Cytoplasm model enabled\")\n","\n","if model_choice == \"Cytoplasm3\":\n"," channels=[segment_channel,nuclear_channel]\n"," model = models.Cellpose(gpu=True, model_type=\"cyto3\")\n"," print(\"Cytoplasm 3 model enabled\")\n","\n","if model_choice == \"Cytoplasm2\":\n"," channels=[segment_channel,nuclear_channel]\n"," model = models.Cellpose(gpu=True, model_type=\"cyto2\")\n"," print(\"Cytoplasm2 model enabled\")\n","\n","if model_choice == \"Cytoplasm2_Omnipose\":\n"," channels=[segment_channel,nuclear_channel]\n"," model = models.Cellpose(gpu=True, model_type=\"cyto2_omni\")\n"," print(\"Cytoplasm2_Omnipose model enabled\")\n","\n","if model_choice == \"Nuclei\":\n"," channels=[segment_channel,0]\n"," model = models.Cellpose(gpu=True, model_type=\"nuclei\")\n"," print(\"Nuclei model enabled\")\n","\n","if model_choice == \"LiveCell\":\n"," channels=[segment_channel,0]\n"," model = models.CellposeModel(gpu=True, model_type=\"livecell\")\n"," print(\"LiveCell model enabled\")\n","\n","if model_choice == \"Bacteria_Omnipose\":\n"," channels=[segment_channel,nuclear_channel]\n"," model = models.Cellpose(gpu=True, model_type=\"bact_omni\")\n"," Object_diameter = 0\n"," print(\"Bacteria_omnipose model enabled\")\n","\n","if model_choice == \"TissueNet\":\n"," channels=[segment_channel,nuclear_channel]\n"," model = models.CellposeModel(gpu=True, model_type='tissuenet')\n"," print(\"TissueNet model enabled\")\n","\n","if model_choice == \"Own_model\":\n"," channels=[segment_channel,nuclear_channel]\n"," model = models.CellposeModel(gpu=True, pretrained_model=Prediction_model)\n","\n"," print(\"Own model enabled\")\n","\n","if Object_diameter is 0:\n"," Object_diameter = None\n"," print(\"The cell size will be estimated automatically for each image\")\n","\n","if Data_type == \"Single_Images\" :\n","\n"," print('--------------------------------------------------------------')\n"," @interact\n"," def preview_results(file = os.listdir(Data_folder)):\n"," source_image = io.imread(os.path.join(Data_folder, file))\n","\n"," if model_choice == \"Own_model\" or model_choice == \"LiveCell\" or model_choice == \"TissueNet\":\n"," masks, flows, styles = model.eval(source_image, diameter=Object_diameter, flow_threshold=Flow_threshold,cellprob_threshold=mask_threshold, channels=channels)\n","\n"," else:\n"," masks, flows, styles, diams = model.eval(source_image, diameter=Object_diameter, flow_threshold=Flow_threshold,cellprob_threshold=mask_threshold, channels=channels)\n","\n"," flowi = flows[0]\n"," fig = plt.figure(figsize=(20,10))\n"," plot.show_segmentation(fig, source_image, masks, flowi, channels=channels)\n"," plt.tight_layout()\n"," plt.show()\n","\n","\n"," def batch_process():\n"," print(\"Your images are now beeing processed\")\n","\n"," for name in os.listdir(Data_folder):\n"," print(\"Performing prediction on: \"+name)\n"," image = io.imread(Data_folder+\"/\"+name)\n"," short_name = os.path.splitext(name)\n","\n"," if model_choice == \"Own_model\" or model_choice == \"LiveCell\" or model_choice == \"TissueNet\":\n"," masks, flows, styles = model.eval(image, diameter=Object_diameter, flow_threshold=Flow_threshold,cellprob_threshold=mask_threshold, channels=channels)\n"," else:\n"," masks, flows, styles, diams = model.eval(image, diameter=Object_diameter, flow_threshold=Flow_threshold,cellprob_threshold=mask_threshold, channels=channels)\n","\n"," os.chdir(Result_folder)\n"," imsave(str(short_name[0])+\"_mask.tif\", masks)\n","\n"," im = interact_manual(batch_process)\n"," im.widget.children[0].description = 'Process your images'\n"," im.widget.children[0].style.button_color = 'yellow'\n"," display(im)\n","\n","if Data_type == \"Stacks (2D + t)\" :\n"," print(\"Stacks (2D + t) are now beeing predicted\")\n","\n"," print('--------------------------------------------------------------')\n"," @interact\n"," def preview_results_stacks(file = os.listdir(Data_folder)):\n"," timelapse = imread(Data_folder+\"/\"+file)\n","\n"," if model_choice == \"Own_model\" or model_choice == \"LiveCell\" or model_choice == \"TissueNet\":\n"," masks, flows, styles = model.eval(timelapse[0], diameter=Object_diameter, flow_threshold=Flow_threshold,cellprob_threshold=mask_threshold, channels=channels)\n"," else:\n"," masks, flows, styles, diams = model.eval(timelapse[0], diameter=Object_diameter, flow_threshold=Flow_threshold,cellprob_threshold=mask_threshold, channels=channels)\n","\n"," flowi = flows[0]\n"," fig = plt.figure(figsize=(20,10))\n"," plot.show_segmentation(fig, timelapse[0], masks, flowi, channels=channels)\n"," plt.tight_layout()\n"," plt.show()\n","\n"," def batch_process_stack():\n"," print(\"Your images are now beeing processed\")\n"," for image in os.listdir(Data_folder):\n"," print(\"Performing prediction on: \"+image)\n"," timelapse = imread(Data_folder+\"/\"+image)\n"," short_name = os.path.splitext(image)\n"," n_timepoint = timelapse.shape[0]\n"," prediction_stack = np.zeros((n_timepoint, timelapse.shape[1], timelapse.shape[2]))\n","\n"," for t in range(n_timepoint):\n"," print(\"Frame number: \"+str(t))\n"," img_t = timelapse[t]\n","\n"," if model_choice == \"Own_model\" or model_choice == \"LiveCell\" or model_choice == \"TissueNet\":\n"," masks, flows, styles = model.eval(img_t, diameter=Object_diameter, flow_threshold=Flow_threshold,cellprob_threshold=mask_threshold, channels=channels)\n"," else:\n"," masks, flows, styles, diams = model.eval(img_t, diameter=Object_diameter, flow_threshold=Flow_threshold,cellprob_threshold=mask_threshold, channels=channels)\n","\n","\n"," prediction_stack[t] = masks\n","\n"," prediction_stack = img_as_float32(prediction_stack, force_copy=False)\n"," os.chdir(Result_folder)\n"," imsave(str(short_name[0])+\".tif\", prediction_stack, compression ='zlib')\n"," del prediction_stack\n","\n"," im = interact_manual(batch_process_stack)\n"," im.widget.children[0].description = 'Process your images'\n"," im.widget.children[0].style.button_color = 'yellow'\n"," display(im)\n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"rk7GNKr5tYBY"},"source":["\n","\n","## **6.2 Generate prediction(s) from unseen dataset (3D stacks)**\n","---\n","\n","The current trained model (from section 4.3) can now be used to process images. If an older model needs to be used, please untick the **Use_the_current_trained_model** box and enter the model's name and path to use. Predicted output images are saved in your **Prediction_folder** folder as restored image stacks (ImageJ-compatible TIFF images).\n","\n","**`Data_folder`:** This folder should contain the images that you want to predict using the network that you will train.\n","\n","**`Result_folder`:** This folder will contain the predicted output.\n","\n","**`model_choice`:** Choose the model to use to make predictions. This model needs to be a Cellpose model. You can also use the pretrained models already available in cellpose:\n","\n","- The cytoplasm model in cellpose is trained on two-channel images, where the first channel is the channel to segment, and the second channel is an optional nuclear channel.\n","- The cytoplasm2 model is an updated cytoplasm model trained with user-submitted images.\n","\n","- The nuclear model in cellpose is trained on two-channel images, where the first channel is the channel to segment, and the second channel is always set to an array of zeros.\n","\n","\n","**`Channel_to_segment`:** Choose the channel to segment. If using single-channel grayscale images, choose \"Grayscale\".\n","\n","**`Nuclear_channel`:** If you are using a model that segment the \"cytoplasm\", you can use a nuclear channel to aid the segmentation.\n","\n","**`Object_diameter`:** Indicate the diameter of the objects (cells or Nuclei) you want to segment (in pixel). Do not input \"0\" to perform 3D predictions.\n","\n","**`Anisotropy`:** Optional rescaling factor to use when the images to segment are not isotropic (e.g. set to 2.0 if Z is sampled half as dense as X or Y). **Default value: 1**\n","\n","**`Min_size`:** Minimum number of pixels per mask. Can be turned off using -1. **Default value: 15**\n","\n","**`mask_threshold`:** The pixels greater than the mask_threshold are used to run dynamics and determine masks. Decrease this threshold if cellpose is not returning as many masks as you'd expect. Similarly, increase this threshold if cellpose is returning too many masks, particularly from dim areas. **Default value: 0.0**\n"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"Qf2Mn-2Yo7tI"},"outputs":[],"source":["from cellpose import utils\n","from numba.experimental import jitclass\n","from numba import jit, float64\n","\n","\n","#@markdown ### Provide the path to your dataset and to the folder where the predictions are saved, then play the cell to predict outputs from your unseen images.\n","\n","Data_folder = \"\" #@param {type:\"string\"}\n","Result_folder = \"\" #@param {type:\"string\"}\n","\n","#@markdown ###What model do you want to use?\n","model_choice = \"Cytoplasm\" #@param [\"Cytoplasm\",\"Cytoplasm2\", \"Nuclei\", \"Own_model\"]\n","\n","\n","#@markdown ####If using your own model, please provide the path to the model (not the folder):\n","\n","Prediction_model = \"\" #@param {type:\"string\"}\n","\n","#@markdown ### What channel do you want to segment?\n","\n","Channel_to_segment= \"Grayscale\" #@param [\"Grayscale\", \"Blue\", \"Green\", \"Red\"]\n","\n","# @markdown ###If you chose the model \"cytoplasm\" indicate if you also have a nuclear channel that can be used to aid the segmentation.\n","\n","Nuclear_channel= \"None\" #@param [\"None\", \"Blue\", \"Green\", \"Red\"]\n","\n","#@markdown ###How do you want to generate the 3D labels?\n","\n","#Choose_3D_mode = \"2D predictions and stitching\" #@param [\"3D predictions\", \"2D predictions and stitching\"]\n","\n","Choose_3D_mode = \"3D predictions\" #@param [\"3D predictions\"]\n","\n","#@markdown ### Segmentation parameters:\n","Object_diameter = 10#@param {type:\"number\"}\n","Anisotropy = 1 #@param {type:\"number\"}\n","Min_size = 200#@param {type:\"number\"}\n","mask_threshold=0 #@param {type:\"slider\", min:-6, max:6, step:1}\n","\n","##@markdown ### Additional parameters for 2D predictions and stitching:\n","\n","#Flow_threshold = 0.5 #@param {type:\"slider\", min:0.1, max:1.1, step:0.1}\n","#Stitch_threshold= 0.1#@param {type:\"slider\", min:0, max:1.0, step:0.1}\n","\n","#failsafes\n","if Choose_3D_mode == \"3D predictions\":\n"," do_3D = True\n"," Stitch_threshold= 0\n","\n"," if Object_diameter == 0:\n"," Object_diameter= 40\n"," print(\"Object_diameter set to 40. If you want to use a different Object_diameter enable it in your segmentation parameters\")\n","\n","else:\n"," do_3D = False\n"," Anisotropy = 0\n","\n","if Choose_3D_mode == \"2D predictions and stitching\" and Stitch_threshold== 0:\n"," Stitch_threshold= 0.5\n"," print(\"Stitch_threshold has now automatically be enabled with an IoU of 0.5. If you want to use a different threshold enable it in your segmentation parameters\")\n","\n","\n","if Channel_to_segment == \"Grayscale\":\n"," segment_channel = 0\n","\n","if Channel_to_segment == \"Blue\":\n"," segment_channel = 3\n","\n","if Channel_to_segment == \"Green\":\n"," segment_channel = 2\n","\n","if Channel_to_segment == \"Red\":\n"," segment_channel = 1\n","\n","if Nuclear_channel == \"Blue\":\n"," nuclear_channel = 3\n","\n","if Nuclear_channel == \"Green\":\n"," nuclear_channel = 2\n","\n","if Nuclear_channel == \"Red\":\n"," nuclear_channel = 1\n","\n","if Nuclear_channel == \"None\":\n"," nuclear_channel = 0\n","\n","if model_choice == \"Cytoplasm\":\n"," channels=[segment_channel,nuclear_channel]\n"," model = models.Cellpose(gpu=True, model_type=\"cyto\")\n"," print(\"Cytoplasm model enabled\")\n","\n","if model_choice == \"Cytoplasm2\":\n"," channels=[segment_channel,nuclear_channel]\n"," model = models.Cellpose(gpu=True, model_type=\"cyto2\")\n"," print(\"Cytoplasm2 model enabled\")\n","\n","if model_choice == \"Nuclei\":\n"," channels=[segment_channel,0]\n"," model = models.Cellpose(gpu=True, model_type=\"nuclei\")\n"," print(\"Nuclei model enabled\")\n","\n","if model_choice == \"Cytoplasm2_Omnipose\":\n"," channels=[segment_channel,nuclear_channel]\n"," model = models.Cellpose(gpu=True, model_type=\"cyto2_omni\")\n"," print(\"Cytoplasm2_Omnipose model enabled\")\n","\n","if model_choice == \"Bacteria_Omnipose\":\n"," channels=[segment_channel,nuclear_channel]\n"," model = models.Cellpose(gpu=True, model_type=\"bact_omni\")\n"," print(\"Bacteria_omnipose model enabled\")\n","\n","if model_choice == \"Own_model\":\n"," channels=[segment_channel,nuclear_channel]\n"," model = models.CellposeModel(gpu=True, pretrained_model=Prediction_model, torch=True, diam_mean=30.0, net_avg=True, device=None, residual_on=True, style_on=True, concatenation=False)\n","\n"," print(\"Own model enabled\")\n","\n","if Object_diameter is 0:\n"," Object_diameter = None\n"," print(\"The cell size will be estimated automatically for each image\")\n","\n","if Choose_3D_mode == \"2D predictions and stitching\":\n","\n"," for image in os.listdir(Data_folder):\n"," print(\"Performing prediction on: \"+image)\n"," stack = imread(Data_folder+\"/\"+image)\n"," short_name = os.path.splitext(image)\n"," n_plane = stack.shape[0]\n"," prediction_stack = np.zeros((n_plane, stack.shape[1], stack.shape[2]))\n","\n"," for t in range(n_plane):\n"," print(\"Z plane number: \"+str(t))\n"," img_t = stack[t]\n","\n"," if model_choice == \"Own_model\":\n"," masks, flows, styles = model.eval(img_t, diameter=Object_diameter, flow_threshold=Flow_threshold,cellprob_threshold=mask_threshold, channels=channels, do_3D=do_3D, anisotropy = Anisotropy, stitch_threshold=Stitch_threshold, min_size=Min_size)\n"," else:\n"," masks, flows, styles, diams = model.eval(img_t, diameter=Object_diameter, flow_threshold=Flow_threshold,cellprob_threshold=mask_threshold, channels=channels, do_3D=do_3D, anisotropy = Anisotropy, stitch_threshold=Stitch_threshold, min_size=Min_size)\n","\n"," prediction_stack = masks\n","\n"," os.chdir(Result_folder)\n"," imsave(str(short_name[0])+\".tif\", prediction_stack)\n","\n","\n","if Choose_3D_mode == \"3D predictions\":\n","\n"," for image in os.listdir(Data_folder):\n"," print(\"Performing prediction on: \"+image)\n"," stack = imread(Data_folder+\"/\"+image)\n"," short_name = os.path.splitext(image)\n","\n"," if model_choice == \"Own_model\":\n"," masks, flows, styles = model.eval(stack, diameter=Object_diameter, channels=channels, do_3D=True, anisotropy = Anisotropy, min_size=Min_size, cellprob_threshold=mask_threshold)\n"," else:\n"," masks, flows, styles, diams = model.eval(stack, diameter=Object_diameter, channels=channels, do_3D=True, anisotropy = Anisotropy, min_size=Min_size, cellprob_threshold=mask_threshold)\n","\n"," prediction_stack_32 = img_as_float32(masks, force_copy=False)\n"," os.chdir(Result_folder)\n"," imsave(str(short_name[0])+\".tif\", prediction_stack_32)\n","\n","\n"," def f(Z_plane):\n"," flowi = flows[0]\n"," fig = plt.figure(figsize=(20,10))\n"," plot.show_segmentation(fig, stack[Z_plane], masks[Z_plane], flowi[Z_plane], channels=channels)\n"," plt.tight_layout()\n"," plt.show()\n","\n"," print('--------------------------------------------------------------')\n"," interact(f, Z_plane=widgets.IntSlider(min=0, max=masks.shape[0], step=1, value=0));\n"]},{"cell_type":"markdown","metadata":{"id":"hvkd66PldsXB"},"source":["## **6.3. Download your predictions**\n","---\n","\n","**Store your data** and ALL its results elsewhere by downloading it from Google Drive and after that clean the original folder tree (datasets, results, trained model etc.) if you plan to train or use new networks. Please note that the notebook will otherwise **OVERWRITE** all files which have the same name."]},{"cell_type":"markdown","metadata":{"id":"dHUKCoSZ7dzV"},"source":["# **7. Version log**\n","---\n","**v1.16.3**:\n","* Cyto3 added\n","* --fast mode removed\n","\n","**v1.16.2**:\n","* Debug in load a model or run QC with the current model (list of files avoids pdf and QC_model is checked rather than the model type)\n","\n","**v1.16.1**:\n","* Replaced all absolute pathing with relative pathing\n","\n","**v1.16**:\n","* Cellpose 2.2 is now used by default\n","* LiveCell and TissueNet models added\n","* A few minor text change\n","\n","\n","**v1.15.4**:\n","* ZIP compression when saving label images removed\n","\n","**v1.15.3**:\n","* A few minor text change\n","\n","**v1.15.2**:\n","\n","* !pip install cellpose==1.0.2\n","\n","* add verbose option and fix issues when less than 10 images are used for training.\n","\n","\n","\n","**v1.15.1**:\n","* !pip install opencv-python-headless==4.1.2.30\n","\n","\n","**v1.15**:\n","* Cellpose updated to 0.7.\n","* Omnipose bacteria and Cyto2 models added.\n","* Various dependencies issues fixed.\n","\n","\n","**v1.14**:\n","* Added the possibility to perform 3D predictions (section 6.2).\n","\n","**v1.13**: \n","\n","* Training now uses TORCH.\n","* This version also now includes built-in version check and the version log that you're reading now."]},{"cell_type":"markdown","metadata":{"id":"u4pcBe8Z3T2J"},"source":["# **Thank you for using Cellpose 2D!**"]}],"metadata":{"accelerator":"GPU","colab":{"machine_shape":"hm","provenance":[{"file_id":"https://github.com/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/Cellpose_2D_ZeroCostDL4Mic.ipynb","timestamp":1742283178934}]},"kernelspec":{"display_name":"Python 3","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":"3.6.4"},"widgets":{"application/vnd.jupyter.widget-state+json":{"f768803545d041638b66941d850b32cd":{"model_module":"@jupyter-widgets/controls","model_name":"VBoxModel","model_module_version":"1.5.0","state":{"_dom_classes":["widget-interact"],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"VBoxModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"VBoxView","box_style":"","children":["IPY_MODEL_790d6e6e473a46e78ffbfc1f72a931ed","IPY_MODEL_dc90ae8a475c46f982be4967a6a83cb1"],"layout":"IPY_MODEL_02f07ea9cda84469b0baf05bb6db2c8a"}},"790d6e6e473a46e78ffbfc1f72a931ed":{"model_module":"@jupyter-widgets/controls","model_name":"DropdownModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DropdownModel","_options_labels":["C2-Slide1.sld - Capture 6 - Position 1.tif","C2-Slide1.sld - Capture 6 - Position 2.tif","C2-Slide1.sld - Capture 6 - Position 4.tif","C2-Slide1.sld - Capture 6 - Position 3.tif","C2-Slide1.sld - Capture 6 - Position 6.tif","C2-Slide1.sld - Capture 6 - Position 5.tif","C2-Slide1.sld - Capture 6 - Position 8.tif","C2-Slide1.sld - Capture 6 - Position 7.tif","C2-Slide1.sld - Capture 6 - Position 9.tif","C2-Slide1.sld - Capture 6 - Position 10.tif","C2-Slide1.sld - Capture 6 - Position 11.tif","C2-Slide1.sld - Capture 6 - Position 12.tif","C2-Slide1.sld - Capture 6 - Position 13.tif","C2-Slide1.sld - Capture 6 - Position 15.tif","C2-Slide1.sld - Capture 6 - Position 14.tif"],"_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"DropdownView","description":"file","description_tooltip":null,"disabled":false,"index":0,"layout":"IPY_MODEL_c2166230f60e4a72b094a9b20d79c774","style":"IPY_MODEL_8d43f28e23ed45b08129ed41526b4ca1"}},"dc90ae8a475c46f982be4967a6a83cb1":{"model_module":"@jupyter-widgets/output","model_name":"OutputModel","model_module_version":"1.0.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/output","_model_module_version":"1.0.0","_model_name":"OutputModel","_view_count":null,"_view_module":"@jupyter-widgets/output","_view_module_version":"1.0.0","_view_name":"OutputView","layout":"IPY_MODEL_f5f6be3714e4455bb60cb90716dfe132","msg_id":"","outputs":[{"output_type":"display_data","data":{"text/plain":"
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