Updated scripts and requirements for python3.12 upgrade

.gitignore

@@ -1,2 +1,60 @@
 /Recipes/__pycache__
-/Utilities/__pycache__
+/Utilities/__pycache__
+**/outputs/*.tif
+**/outputs/*.tiff
+**/outputs/*.aiviaexperiment
+
+
+# These are some examples of commonly ignored file patterns.
+# You should customize this list as applicable to your project.
+# Learn more about .gitignore:
+#     https://www.atlassian.com/git/tutorials/saving-changes/gitignore
+
+# Node artifact files
+node_modules/
+dist/
+
+# Compiled Java class files
+*.class
+
+# Compiled Python bytecode
+*.py[cod]
+
+# Log files
+*.log
+
+# Package files
+*.jar
+
+# Maven
+target/
+dist/
+
+# JetBrains IDE
+.idea/
+
+# Unit test reports
+TEST*.xml
+
+# Generated by MacOS
+.DS_Store
+
+# Generated by Windows
+Thumbs.db
+
+# Applications
+*.app
+*.exe
+*.war
+
+# Large media files
+*.mp4
+*.avi
+*.flv
+*.mov
+*.wmv
+*.csv
+*.db
+
+# python files
+*.pyc

PythonEnvForAivia/FirstTimeSetup.py

@@ -28,10 +28,10 @@ def run(params):
         env_dir.mkdir(parents=False, exist_ok=True)
         subprocess.check_call([str(Path(sys.executable).parent / 'Scripts/virtualenv.exe'), f'{env_dir}'])
 
-        # copy essential python packages(python39.zip) to virtual environment
+        # copy essential python packages(python312.zip) to virtual environment
         # see https://github.com/pypa/virtualenv/issues/1185
-        if not os.path.exists(env_dir/'Scripts/python39.zip'):
-            copyfile(Path(sys.executable).parent / 'python39.zip', env_dir/'Scripts/python39.zip')
+        if not os.path.exists(env_dir/'Scripts/python312.zip'):
+            copyfile(Path(sys.executable).parent / 'python312.zip', env_dir/'Scripts/python312.zip')
 
         # install requirements
         mess = 'Python packages will now be installed. An internet connection is needed.\n\n' \
@@ -41,6 +41,8 @@ def run(params):
 
         pip_path = env_dir / 'Scripts' / 'pip.exe'
         requirement_dir = pathlib.Path(os.path.dirname(os.path.realpath(__file__)))
+        # subprocess.check_call(
+        #     [str(pip_path), 'install', 'setuptools==70.0.0'])
         subprocess.check_call(
             [str(pip_path), 'install', '-r', str(requirement_dir/'requirements.txt')])
 

PythonEnvForAivia/README.md

@@ -1,8 +1,8 @@
-# Python virtual Environment For Aivia
+# Python Virtual Environment for Aivia
 
 ## General Information
 
-The FirstTimeSetup.py script will ensure a virtual environment is created to run the python recipes for Aivia with the appropriate packages.
+The [`FirstTimeSetup.py`](/PythonEnvForAivia/FirstTimeSetup.py) script will ensure a virtual environment is created to run the python recipes for Aivia with the appropriate packages.
 No further install should be needed. Hence, the script is run once only.
 
 The recipes are then containing some code to:
@@ -13,8 +13,7 @@ The recipes are then containing some code to:
 
 ## Requirements
 
-* Python 3.9 - comes with Aivia
-
+* Python 3.12 - comes with Aivia
 * Accept the risks that come from running Python Scripts you download from the internet. These scripts are provided to you to use at your own risk. 
 
 ## Installation
@@ -33,16 +32,16 @@ The tree structure of the folder is as follow
   └───Recipes
       └───[category subfolders]
           └───... .py
+
 ```
 
 ## Execution
 
-1. Load `FirstTimeSetup.py` in Aivia by "File>Open" or drag-and-drop
-
+1. Load [`FirstTimeSetup.py`](/PythonEnvForAivia/FirstTimeSetup.py) in Aivia by "File>Open" or drag-and-drop
 2. Load any 2D image in Aivia
-
 3. Click on "Start" button and wait for "Process Completed" message at the bottom of the recipe console
     1. Please make sure that you have the Internet connection and wait if bandwidth is slow.
+    2. At the end of this step, a new folder called `env` should be created in the `PythonEnvForAivia` folder.  This will be the folder that contains a virtual environment identical to Aivia's embedded python that all future Recipes will run off of.
 
 
 ## Returns

Recipes/CollectImageMetrics/ImageComparisonMetrics.py → PythonEnvForAivia/Recipes/CollectImageMetrics/ImageComparisonMetrics.py

@@ -1,98 +1,103 @@
-import os.path
-import numpy as np
-from skimage.io import imread, imsave
-from skimage.metrics import mean_squared_error, structural_similarity
-from skimage.exposure import match_histograms, rescale_intensity
-import ctypes
-
-"""
-Calculates SSIM map as a result of the comparison of 2 channels and metrics values (in the log file). 
-
-For the output image, it is highly recommended to use LUT color mapping to better see the variations in the SSIM values
-All real SSIM values (ranging from 0 to 1) can be retrieved from the map doing the following: divide intensities by 255 if image is 8-bit, or by 65535 if 16-bit.
-
-Side note: MSE and mean SSIM (and NRMSE, PSNR) values are output in the log
-To be able to see the printed info in the log file, set:
-File > Options > Logging > Verbosity = everything
-
-Sources: 
-https://scikit-image.org/docs/dev/api/skimage.metrics.html?highlight=structural#skimage.metrics.structural_similarity
-https://scikit-image.org/docs/dev/auto_examples/color_exposure/plot_histogram_matching.html#sphx-glr-auto-examples-color-exposure-plot-histogram-matching-py
-
-
-Requirements
-------------
-numpy (comes with Aivia installer)
-scikit-image (comes with Aivia installer)
-
-Parameters
-----------
-First input: image to compare (e.g.Deep Learning restored image)
-Second input: reference (e.g. Ground Truth image), the one adjusted by histogram matching.
-IMPORTANT: Input channels need to have the same bit depth
-
-Returns
--------
-First output: calculated SSIM map
-Second output: reference image transformed with histogram matching
-
-"""
-
-# [INPUT Name:inputGTImagePath Type:string DisplayName:'Input Ground Truth Image']
-# [INPUT Name:inputRTImagePath Type:string DisplayName:'Input Restored Image']
-# [OUTPUT Name:resultPathAdj Type:string DisplayName:'GT Hist match image']
-# [OUTPUT Name:resultPath Type:string DisplayName:'SSIM image']
-def run(params):
-    RTimageLocation = params['inputRTImagePath']
-    GTimageLocation = params['inputGTImagePath']
-    resultLocation = params['resultPath']
-    resultLocationAdj = params['resultPathAdj']
-
-    # Checking existence of temporary files (individual channels)
-    if not os.path.exists(RTimageLocation):
-        print(f'Error: {RTimageLocation} does not exist')
-        return; 
-    if not os.path.exists(GTimageLocation):
-        print(f'Error: {GTimageLocation} does not exist')
-        return; 
-
-    # Loading input images
-    RTData = imread(RTimageLocation)
-    GTData = imread(GTimageLocation)
-    print(f'Dimensions of Restored image: {RTData.shape}')
-    print(f'Dimensions of GT image: {GTData.shape}')
-
-    # Checking dtype is the same for both input channels
-    if GTData.dtype != RTData.dtype:
-        error_mes = "The bit depth of your input channels is not the same. Convert one of them and retry."
-        ctypes.windll.user32.MessageBoxW(0, error_mes, 'Error', 0)
-        sys.exit(error_mes)
-
-    # Histogram matching
-    matched_GTData = match_histograms(GTData, RTData).astype(RTData.dtype)
-
-    # MSE measurement
-    # valMSE = skimage.measure.compare_mse(RTData, GTData) # deprecated in scikit-image 0.18 
-    valMSE = mean_squared_error(RTData, matched_GTData)
-    print(f'___ MSE = {valMSE} ___')    # Value appears in the log if Verbosity option is set to 'Everything'
-
-    # SSIM measurement
-    outFullSSIM = structural_similarity(RTData, matched_GTData, full=True)
-
-    # Extracting mean value (first item)
-    outMeanSSIM = outFullSSIM[0]
-    print(f'___ Mean SSIM = {outMeanSSIM} ___')
-
-    # Extracting map (second item)
-    outSSIM = outFullSSIM[1]
-    print(f'Bit depth of SSIM array: {outSSIM.dtype}')
-
-    # Convert output array whose range is [0-1] to adjusted bit range (8- or 16-bit) if necessary
-    if RTData.dtype != np.dtype('float64') and RTData.dtype != np.dtype('float32'):
-        outputData = rescale_intensity(outSSIM, in_range=(0, 1), out_range=(0, np.iinfo(RTData.dtype).max))
-        outputData = outputData.astype(RTData.dtype)
-    else:
-        outputData = outSSIM
-
-    imsave(resultLocation, outputData)  
-    imsave(resultLocationAdj, matched_GTData)
+import os.path
+import numpy as np
+from skimage.io import imread, imsave
+from skimage.metrics import mean_squared_error, structural_similarity
+from skimage.exposure import match_histograms, rescale_intensity
+import ctypes
+
+"""
+Calculates SSIM map as a result of the comparison of 2 channels and metrics values (in the log file). 
+
+For the output image, it is highly recommended to use LUT color mapping to better see the variations in the SSIM values
+All real SSIM values (ranging from 0 to 1) can be retrieved from the map doing the following: divide intensities by 255 if image is 8-bit, or by 65535 if 16-bit.
+
+Side note: MSE and mean SSIM (and NRMSE, PSNR) values are output in the log
+To be able to see the printed info in the log file, set:
+File > Options > Logging > Verbosity = everything
+
+Sources: 
+https://scikit-image.org/docs/dev/api/skimage.metrics.html?highlight=structural#skimage.metrics.structural_similarity
+https://scikit-image.org/docs/dev/auto_examples/color_exposure/plot_histogram_matching.html#sphx-glr-auto-examples-color-exposure-plot-histogram-matching-py
+
+
+Requirements
+------------
+numpy (comes with Aivia installer)
+scikit-image (comes with Aivia installer)
+
+Parameters
+----------
+First input: image to compare (e.g.Deep Learning restored image)
+Second input: reference (e.g. Ground Truth image), the one adjusted by histogram matching.
+IMPORTANT: Input channels need to have the same bit depth
+
+Returns
+-------
+First output: calculated SSIM map
+Second output: reference image transformed with histogram matching
+
+"""
+
+# [INPUT Name:inputGTImagePath Type:string DisplayName:'Input Ground Truth Image']
+# [INPUT Name:inputRTImagePath Type:string DisplayName:'Input Restored Image']
+# [OUTPUT Name:resultPathAdj Type:string DisplayName:'GT Hist match image']
+# [OUTPUT Name:resultPath Type:string DisplayName:'SSIM image']
+def run(params):
+    RTimageLocation = params['inputRTImagePath']
+    GTimageLocation = params['inputGTImagePath']
+    resultLocation = params['resultPath']
+    resultLocationAdj = params['resultPathAdj']
+    channel_axis=params.get('channel_axis')
+    if channel_axis == "None":
+        channel_axis = None
+    else:
+        channel_axis = int(channel_axis)
+
+    # Checking existence of temporary files (individual channels)
+    if not os.path.exists(RTimageLocation):
+        print(f'Error: {RTimageLocation} does not exist')
+        return; 
+    if not os.path.exists(GTimageLocation):
+        print(f'Error: {GTimageLocation} does not exist')
+        return; 
+
+    # Loading input images
+    RTData = imread(RTimageLocation)
+    GTData = imread(GTimageLocation)
+    print(f'Dimensions of Restored image: {RTData.shape}')
+    print(f'Dimensions of GT image: {GTData.shape}')
+
+    # Checking dtype is the same for both input channels
+    if GTData.dtype != RTData.dtype:
+        error_mes = "The bit depth of your input channels is not the same. Convert one of them and retry."
+        ctypes.windll.user32.MessageBoxW(0, error_mes, 'Error', 0)
+        sys.exit(error_mes)
+
+    # Histogram matching
+    matched_GTData = match_histograms(GTData, RTData).astype(RTData.dtype)
+
+    # MSE measurement
+    # valMSE = skimage.measure.compare_mse(RTData, GTData) # deprecated in scikit-image 0.18 
+    valMSE = mean_squared_error(RTData, matched_GTData)
+    print(f'___ MSE = {valMSE} ___')    # Value appears in the log if Verbosity option is set to 'Everything'
+
+    # SSIM measurement
+    outFullSSIM = structural_similarity(RTData, matched_GTData, full=True, channel_axis=channel_axis)
+
+    # Extracting mean value (first item)
+    outMeanSSIM = outFullSSIM[0]
+    print(f'___ Mean SSIM = {outMeanSSIM} ___')
+
+    # Extracting map (second item)
+    outSSIM = outFullSSIM[1]
+    print(f'Bit depth of SSIM array: {outSSIM.dtype}')
+
+    # Convert output array whose range is [0-1] to adjusted bit range (8- or 16-bit) if necessary
+    if RTData.dtype != np.dtype('float64') and RTData.dtype != np.dtype('float32'):
+        outputData = rescale_intensity(outSSIM, in_range=(0, 1), out_range=(0, np.iinfo(RTData.dtype).max))
+        outputData = outputData.astype(RTData.dtype)
+    else:
+        outputData = outSSIM
+
+    imsave(resultLocation, outputData)  
+    imsave(resultLocationAdj, matched_GTData)