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mv recipes folder into PythonEnvForAivia
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‎Recipes/CollectImageMetrics/CalculateIntersectionOverUnion.py renamed to ‎PythonEnvForAivia/Recipes/CollectImageMetrics/CalculateIntersectionOverUnion.py

File renamed without changes.

‎Recipes/CollectImageMetrics/ImageComparisonMetrics.py renamed to ‎PythonEnvForAivia/Recipes/CollectImageMetrics/ImageComparisonMetrics.py

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import os.path
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import numpy as np
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from skimage.io import imread, imsave
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from skimage.metrics import mean_squared_error, structural_similarity
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from skimage.exposure import match_histograms, rescale_intensity
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import ctypes
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"""
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Calculates SSIM map as a result of the comparison of 2 channels and metrics values (in the log file).
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For the output image, it is highly recommended to use LUT color mapping to better see the variations in the SSIM values
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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.
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Side note: MSE and mean SSIM (and NRMSE, PSNR) values are output in the log
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To be able to see the printed info in the log file, set:
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File > Options > Logging > Verbosity = everything
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Sources:
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https://scikit-image.org/docs/dev/api/skimage.metrics.html?highlight=structural#skimage.metrics.structural_similarity
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https://scikit-image.org/docs/dev/auto_examples/color_exposure/plot_histogram_matching.html#sphx-glr-auto-examples-color-exposure-plot-histogram-matching-py
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Requirements
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------------
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numpy (comes with Aivia installer)
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scikit-image (comes with Aivia installer)
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Parameters
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----------
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First input: image to compare (e.g.Deep Learning restored image)
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Second input: reference (e.g. Ground Truth image), the one adjusted by histogram matching.
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IMPORTANT: Input channels need to have the same bit depth
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Returns
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-------
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First output: calculated SSIM map
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Second output: reference image transformed with histogram matching
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"""
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# [INPUT Name:inputGTImagePath Type:string DisplayName:'Input Ground Truth Image']
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# [INPUT Name:inputRTImagePath Type:string DisplayName:'Input Restored Image']
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# [OUTPUT Name:resultPathAdj Type:string DisplayName:'GT Hist match image']
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# [OUTPUT Name:resultPath Type:string DisplayName:'SSIM image']
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def run(params):
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RTimageLocation = params['inputRTImagePath']
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GTimageLocation = params['inputGTImagePath']
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resultLocation = params['resultPath']
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resultLocationAdj = params['resultPathAdj']
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# Checking existence of temporary files (individual channels)
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if not os.path.exists(RTimageLocation):
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print(f'Error: {RTimageLocation} does not exist')
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return;
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if not os.path.exists(GTimageLocation):
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print(f'Error: {GTimageLocation} does not exist')
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return;
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# Loading input images
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RTData = imread(RTimageLocation)
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GTData = imread(GTimageLocation)
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print(f'Dimensions of Restored image: {RTData.shape}')
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print(f'Dimensions of GT image: {GTData.shape}')
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# Checking dtype is the same for both input channels
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if GTData.dtype != RTData.dtype:
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error_mes = "The bit depth of your input channels is not the same. Convert one of them and retry."
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ctypes.windll.user32.MessageBoxW(0, error_mes, 'Error', 0)
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sys.exit(error_mes)
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# Histogram matching
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matched_GTData = match_histograms(GTData, RTData).astype(RTData.dtype)
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# MSE measurement
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# valMSE = skimage.measure.compare_mse(RTData, GTData) # deprecated in scikit-image 0.18
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valMSE = mean_squared_error(RTData, matched_GTData)
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print(f'___ MSE = {valMSE} ___') # Value appears in the log if Verbosity option is set to 'Everything'
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# SSIM measurement
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outFullSSIM = structural_similarity(RTData, matched_GTData, full=True)
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# Extracting mean value (first item)
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outMeanSSIM = outFullSSIM[0]
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print(f'___ Mean SSIM = {outMeanSSIM} ___')
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# Extracting map (second item)
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outSSIM = outFullSSIM[1]
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print(f'Bit depth of SSIM array: {outSSIM.dtype}')
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# Convert output array whose range is [0-1] to adjusted bit range (8- or 16-bit) if necessary
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if RTData.dtype != np.dtype('float64') and RTData.dtype != np.dtype('float32'):
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outputData = rescale_intensity(outSSIM, in_range=(0, 1), out_range=(0, np.iinfo(RTData.dtype).max))
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outputData = outputData.astype(RTData.dtype)
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else:
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outputData = outSSIM
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imsave(resultLocation, outputData)
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imsave(resultLocationAdj, matched_GTData)
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import os.path
2+
import numpy as np
3+
from skimage.io import imread, imsave
4+
from skimage.metrics import mean_squared_error, structural_similarity
5+
from skimage.exposure import match_histograms, rescale_intensity
6+
import ctypes
7+
8+
"""
9+
Calculates SSIM map as a result of the comparison of 2 channels and metrics values (in the log file).
10+
11+
For the output image, it is highly recommended to use LUT color mapping to better see the variations in the SSIM values
12+
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.
13+
14+
Side note: MSE and mean SSIM (and NRMSE, PSNR) values are output in the log
15+
To be able to see the printed info in the log file, set:
16+
File > Options > Logging > Verbosity = everything
17+
18+
Sources:
19+
https://scikit-image.org/docs/dev/api/skimage.metrics.html?highlight=structural#skimage.metrics.structural_similarity
20+
https://scikit-image.org/docs/dev/auto_examples/color_exposure/plot_histogram_matching.html#sphx-glr-auto-examples-color-exposure-plot-histogram-matching-py
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22+
23+
Requirements
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------------
25+
numpy (comes with Aivia installer)
26+
scikit-image (comes with Aivia installer)
27+
28+
Parameters
29+
----------
30+
First input: image to compare (e.g.Deep Learning restored image)
31+
Second input: reference (e.g. Ground Truth image), the one adjusted by histogram matching.
32+
IMPORTANT: Input channels need to have the same bit depth
33+
34+
Returns
35+
-------
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First output: calculated SSIM map
37+
Second output: reference image transformed with histogram matching
38+
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"""
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# [INPUT Name:inputGTImagePath Type:string DisplayName:'Input Ground Truth Image']
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# [INPUT Name:inputRTImagePath Type:string DisplayName:'Input Restored Image']
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# [OUTPUT Name:resultPathAdj Type:string DisplayName:'GT Hist match image']
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# [OUTPUT Name:resultPath Type:string DisplayName:'SSIM image']
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def run(params):
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RTimageLocation = params['inputRTImagePath']
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GTimageLocation = params['inputGTImagePath']
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resultLocation = params['resultPath']
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resultLocationAdj = params['resultPathAdj']
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# Checking existence of temporary files (individual channels)
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if not os.path.exists(RTimageLocation):
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print(f'Error: {RTimageLocation} does not exist')
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return;
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if not os.path.exists(GTimageLocation):
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print(f'Error: {GTimageLocation} does not exist')
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return;
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# Loading input images
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RTData = imread(RTimageLocation)
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GTData = imread(GTimageLocation)
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print(f'Dimensions of Restored image: {RTData.shape}')
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print(f'Dimensions of GT image: {GTData.shape}')
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# Checking dtype is the same for both input channels
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if GTData.dtype != RTData.dtype:
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error_mes = "The bit depth of your input channels is not the same. Convert one of them and retry."
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ctypes.windll.user32.MessageBoxW(0, error_mes, 'Error', 0)
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sys.exit(error_mes)
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# Histogram matching
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matched_GTData = match_histograms(GTData, RTData).astype(RTData.dtype)
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# MSE measurement
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# valMSE = skimage.measure.compare_mse(RTData, GTData) # deprecated in scikit-image 0.18
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valMSE = mean_squared_error(RTData, matched_GTData)
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print(f'___ MSE = {valMSE} ___') # Value appears in the log if Verbosity option is set to 'Everything'
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# SSIM measurement
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outFullSSIM = structural_similarity(RTData, matched_GTData, full=True)
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# Extracting mean value (first item)
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outMeanSSIM = outFullSSIM[0]
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print(f'___ Mean SSIM = {outMeanSSIM} ___')
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# Extracting map (second item)
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outSSIM = outFullSSIM[1]
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print(f'Bit depth of SSIM array: {outSSIM.dtype}')
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# Convert output array whose range is [0-1] to adjusted bit range (8- or 16-bit) if necessary
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if RTData.dtype != np.dtype('float64') and RTData.dtype != np.dtype('float32'):
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outputData = rescale_intensity(outSSIM, in_range=(0, 1), out_range=(0, np.iinfo(RTData.dtype).max))
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outputData = outputData.astype(RTData.dtype)
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else:
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outputData = outSSIM
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imsave(resultLocation, outputData)
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imsave(resultLocationAdj, matched_GTData)

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