[WIP] Dl4mic

.vscode/settings.json

@@ -0,0 +1,11 @@
+{
+    "python.pythonPath": "/home/ctr26/miniconda3/envs/dl4mic/bin/python",
+    "python.formatting.provider": "black",
+    "python.testing.pytestArgs": [
+        "tests"
+    ],
+    "python.testing.unittestEnabled": false,
+    "python.testing.nosetestsEnabled": false,
+    "python.testing.pytestEnabled": true
+}
+

ColabNotebooks/minimal.ipynb

@@ -0,0 +1,121 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10-final"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.10 64-bit ('tf': conda)",
+   "metadata": {
+    "interpreter": {
+     "hash": "01a8c3e581587ef845b14c27476cc2daada005a8e900d0cc550301b789c363ba"
+    }
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#@markdown ##Run this cell to check if you have GPU access\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": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Notebook_version = ['1.12']\n",
+    "import tensorflow\n",
+    "# ------- Variable specific to N2V -------\n",
+    "from n2v.models import N2VConfig, N2V\n",
+    "from csbdeep.utils import plot_history\n",
+    "from n2v.utils.n2v_utils import manipulate_val_data\n",
+    "from n2v.internals.N2V_DataGenerator import N2V_DataGenerator\n",
+    "from csbdeep.io import save_tiff_imagej_compatible\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 fpdf import FPDF, HTMLMixin\n",
+    "from datetime import datetime\n",
+    "from pip._internal.operations.freeze import freeze\n",
+    "import subprocess\n",
+    "from datetime import datetime\n",
+    "\n",
+    "# Colors for the warning messages\n",
+    "class bcolors:\n",
+    "  WARNING = '\\033[31m'\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",
+    "\n",
+    "# Check if this is the latest version of the notebook\n",
+    "Latest_notebook_version = pd.read_csv(\"https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/Latest_ZeroCostDL4Mic_Release.csv\")\n",
+    "print('Notebook version: '+Notebook_version[0])\n",
+    "strlist = Notebook_version[0].split('.')\n",
+    "Notebook_version_main = strlist[0]+'.'+strlist[1]\n",
+    "if Notebook_version_main == Latest_notebook_version.columns:\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"
+   ]
+  }
+ ]
+}

LICENSE

@@ -1,6 +1,7 @@
 MIT License
 
 Copyright (c) 2020 Quantitative Imaging and Nanobiophysics Group
+=======
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

README.md

@@ -40,3 +40,34 @@ DOI: [https://doi.org/10.1038/s41467-021-22518-0](https://www.nature.com/article
   [8]: https://github.com/HenriquesLab/ZeroCostDL4Mic/blob/master/Wiki_files/VideoDemoScreenshot1.png
   [wikiPage]: https://github.com/HenriquesLab/DeepLearning_Collab/wiki
   [wikiPageContributors]: https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki#contributors
+
+=======
+
+# dl4mic
+
+Packaged form of [ZeroCostDl4Mic](https://github.com/HenriquesLab/ZeroCostDL4Mic) to make the process more platform agnostic.
+Attempts to bundle reusable code and structure model training and prediction into a no-code config file toolset.
+
+
+    pip install git+https://github.com/ctr26/dl4mic
+
+
+Currently working with Noise2Void and Care2D
+
+## Build and test
+
+This project uses poetry to build, test and manage dependnecies:
+
+Quick start:
+    peotry build
+    poetry install
+    poetry run pytest
+
+Note that testing is (rightly) slow due to running model epochs for testing
+
+## Todo:
+
+- Find all the bugs
+- Implement the full roster of ZeroCostDL4Mic models.
+- Command line interface
+- Implement lazy loading of large/uninstalled packages (looking at you pyTorch)

_Dockerfile

@@ -0,0 +1,26 @@
+FROM tensorflow/tensorflow:1.15.5-gpu-jupyter
+
+# --- Jupyter
+
+# install the notebook package
+RUN pip install --no-cache --upgrade pip && \
+    pip install --no-cache notebook
+
+# create user with a home directory
+ARG NB_USER
+ARG NB_UID
+ENV USER ${NB_USER}
+ENV HOME /home/${NB_USER}
+
+RUN adduser --disabled-password \
+    --gecos "Default user" \
+    --uid ${NB_UID} \
+    ${NB_USER}
+WORKDIR ${HOME}
+USER ${USER}
+
+# RUN conda install pip --yes
+
+COPY . .
+
+RUN pip install --no-cache-dir -r requirements.txt

dl4mic/__init__.py

@@ -0,0 +1,53 @@
+# import tensorflow as tf
+# ------- Common variable to all ZeroCostDL4Mic notebooks -------
+
+import numpy as np
+from matplotlib import pyplot as plt
+import urllib
+import os, random
+import shutil
+import zipfile
+from tifffile import imread, imsave
+import time
+import sys
+import wget
+from pathlib import Path
+import pandas as pd
+import csv
+from glob import glob
+from scipy import signal
+from scipy import ndimage
+from skimage import io
+from sklearn.linear_model import LinearRegression
+from skimage.util import img_as_uint
+import matplotlib as mpl
+from skimage.metrics import structural_similarity
+from skimage.metrics import peak_signal_noise_ratio as psnr
+from astropy.visualization import simple_norm
+from skimage import img_as_float32
+from fpdf import FPDF, HTMLMixin
+from datetime import datetime
+from pip._internal.operations.freeze import freeze
+import subprocess
+from datetime import datetime
+
+class bcolors:
+    WARNING = "\033[31m"
+
+
+W = "\033[0m"  # white (normal)
+R = "\033[31m"  # red
+
+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).'
+
+
+from . import models
+
+
+# def __main__():
+#     read_latest_notebook_version()
+
+
+
+
+

dl4mic/assess.py

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+import matplotlib as plt
+from tifffile.tifffile import imread
+from . import models
+import random
+import os
+
+
+def full(Data_folder, Result_folder, Data_type):
+    if not ((Data_folder is None) or (Result_folder is None) or (Data_type is None)):
+        display_random_image(Data_folder, Result_folder, Data_type)
+    pass
+
+
+def display_random_image(Data_folder, Result_folder, Data_type):
+    try:
+        # if Data_folder is not None:
+        file_list = os.listdir(Data_folder)
+        random_choice = random.choice(file_list)
+        x = imread(os.path.join(Data_folder,random_choice))
+
+        os.chdir(Result_folder)
+        y = imread(os.path.join(Result_folder,random_choice))
+
+        if Data_type == models.params.Data_type.SINGLE_IMAGES:
+
+            f = plt.figure(figsize=(16, 8))
+            plt.subplot(1, 2, 1)
+            plt.imshow(x, interpolation="nearest")
+            plt.title("Input")
+            plt.axis("off")
+            plt.subplot(1, 2, 2)
+            plt.imshow(y, interpolation="nearest")
+            plt.title("Predicted output")
+            plt.axis("off")
+            plt.show()
+
+        if Data_type == models.params.Data_type.STACKS:
+
+            f = plt.figure(figsize=(16, 8))
+            plt.subplot(1, 2, 1)
+            plt.imshow(x[1], interpolation="nearest")
+            plt.title("Input")
+            plt.axis("off")
+            plt.subplot(1, 2, 2)
+            plt.imshow(y[1], interpolation="nearest")
+            plt.title("Predicted output")
+            plt.axis("off")
+            plt.show()
+    except FileExistsError:
+        print("Couldn't find a random image")
+    except IndexError:
+        print("Couldn't find a random image")