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carshadicarshadi
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update Filter_Multiple_Images.py
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‎src/main/resources/script_templates/Neuroanatomy/Batch/Filter_Multiple_Images.py‎

Lines changed: 90 additions & 81 deletions
Original file line numberDiff line numberDiff line change
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# @ImageJ ij
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# @String(value="<HTML>This script applies the Frangi Vesselness filter to all TIFF images in a directory.<br>Only 2D or 3D grayscale images are supported. Processing log is shown in Console.", visibility="MESSAGE") msg
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# @String(value="<HTML>This script applies the Tubeness filter to all TIFF images in a directory.<br>Only 2D or 3D grayscale images are supported. Processing log is shown in Console.", visibility="MESSAGE") msg
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# @File(label="Directory containing your images", style="directory") input_dir
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# @String(label="Consider only filenames containing",description="Clear field for no filtering",value="") name_filter
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# @boolean(label="Include subdirectories") recursive
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# @float(label="Size of structures to be filtered (spatially calibrated units)",description="The filter kernel will be determined from the average voxel size") scale
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# @String(label="Size of structures to be filtered (multiple of avg. voxel spacing)",description="The filter kernel will be determined from the average voxel spacing") scales
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# @LogService log
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# @StatusService status
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# @UIService uiservice
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# @DatasetIOService dio
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# @IOService io
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# @Context context
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"""
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file: Filter_Multiple_Images.py
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author: Tiago Ferreira, Cameron Arshadi
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version: 20190525
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info: Bulk filtering of image files using Frangi Vesselness
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file: Filter_Multiple_Images.py
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author: Tiago Ferreira, Cameron Arshadi
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version: 20190525
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info: Bulk filtering of image files using Frangi Vesselness
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"""
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from net.imagej.axis import Axes
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import os
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from java.lang import Runtime
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from net.imglib2.type.numeric.real import FloatType
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from net.imglib2.img.display.imagej import ImageJFunctions
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from net.imagej.axis import Axes
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from sc.fiji.snt.filter import Tubeness
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from io.scif.img import ImgSaver
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def get_image_files(directory, filtering_string, extension):
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"""Returns a list containing the paths of files in the specified
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directory. The list will only include files with the supplied
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extension whose filename contains the specified string."""
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files = []
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for (dirpath, dirnames, filenames) in os.walk(directory):
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for f in filenames:
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if os.path.basename(f).startswith('.'):
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continue
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if filtering_string in f and f.lower().endswith(extension):
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files.append(os.path.join(dirpath, f))
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if not recursive:
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break # do not process subdirectories
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return files
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"""Returns a list containing the paths of files in the specified
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directory. The list will only include files with the supplied
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extension whose filename contains the specified string."""
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files = []
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for (dirpath, dirnames, filenames) in os.walk(directory):
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for f in filenames:
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if os.path.basename(f).startswith('.'):
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continue
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if filtering_string in f and f.lower().endswith(extension):
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files.append(os.path.join(dirpath, f))
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if not recursive:
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break # do not process subdirectories
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return files
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def run():
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# First check that scale parameter is > 0, exiting if not
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if scale <= 0:
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log.error('Please select a value > 0 for the scale parameter. Exiting...')
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return
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# Get all files with specified name filter and extension in the input directory
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d = str(input_dir)
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extension = ".tif"
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files = get_image_files(d, name_filter, extension)
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if not files or len(files) == 0:
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uiservice.showDialog("No files matched the specified criteria", "Error")
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return
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processed = 0
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skipped = 0
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for f in files:
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basename = os.path.basename(f)
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msg = 'Processing file %s: %s...' % (processed + skipped + 1, basename)
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status.showStatus(msg)
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print(msg)
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# Load the input image
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input_image = ij.io().open(f)
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# Verify that the image is 2D/3D and grayscale, skipping it if not
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num_dimensions = input_image.numDimensions()
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if num_dimensions > 3 or int(input_image.getChannels()) > 1:
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log.error('Could not process %s...Only 2D/3D grayscale images are supported' % basename)
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skipped += 1
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continue
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# Convert input image to float, since we are dealing with derivatives
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float_input = ij.op().run("convert.float32", input_image)
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# Obtain spatial calibration of the image
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x_spacing = float_input.averageScale(0)
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y_spacing = float_input.averageScale(1)
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spacing = [x_spacing, y_spacing]
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if num_dimensions == 3 and float_input.axis(2).type() == Axes.Z:
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z_spacing = float_input.averageScale(2)
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spacing.append(z_spacing)
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# Create placeholder image for the output then run the Frangi Vesselness op
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output = ij.op().run("create.img", float_input)
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pixel_scale = scale / (sum(spacing) / len(spacing)) # average voxel size: convert scale to pixel units
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ij.op().run("frangiVesselness", output, float_input, spacing, pixel_scale)
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# Save the result using the same basename as the image, adding "[Frangi].tif"
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# For example, the output for "OP_1.tif" would be named "OP_1[Frangi].tif"
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l = len(f)
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el = len(extension)
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output_filepath = f[0:l - el] + "[Frangi].tif"
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ij.io().save(output, output_filepath)
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processed += 1
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99-
print('Done. %s file(s) processed. %s file(s) skipped...' % (processed, skipped))
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# First check that scale parameters are > 0, exiting if not
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global scales
50+
scales = [float(s) for s in scales.split(',')]
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if not scales or any(s <= 0 for s in scales):
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log.error('Please select values > 0 for the scale parameter. Exiting...')
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return
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# Get all files with specified name filter and extension in the input directory
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d = str(input_dir)
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extension = ".tif"
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files = get_image_files(d, name_filter, extension)
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if not files or len(files) == 0:
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uiservice.showDialog("No files matched the specified criteria", "Error")
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return
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processed = 0
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skipped = 0
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for f in files:
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basename = os.path.basename(f)
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msg = 'Processing file %s: %s...' % (processed + skipped + 1, basename)
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status.showStatus(msg)
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# Load the input image
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input_image = dio.open(f)
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# Verify that the image is 2D/3D and grayscale, skipping it if not
75+
num_dimensions = input_image.numDimensions()
76+
if num_dimensions > 3 or input_image.getChannels() > 1:
77+
log.error('Could not process %s...Only 2D/3D grayscale images are supported' % basename)
78+
skipped += 1
79+
continue
80+
81+
# Obtain spatial calibration of the image
82+
x_spacing = input_image.averageScale(0)
83+
y_spacing = input_image.averageScale(1)
84+
spacing = [x_spacing, y_spacing]
85+
86+
if num_dimensions == 3 and input_image.axis(2).type() == Axes.Z:
87+
z_spacing = input_image.averageScale(2)
88+
spacing.append(z_spacing)
89+
90+
avgsep = sum(spacing) / len(spacing)
91+
scales = [s * avgsep for s in scales]
92+
# Create placeholder image for the output then run the Frangi Vesselness op
93+
output = ij.op().create().img(input_image, FloatType())
94+
# Pass scales in physical units (e.g., um)
95+
op = Tubeness(scales, spacing, Runtime.getRuntime().availableProcessors())
96+
op.compute(input_image, output)
97+
98+
# Save the result using the same basename as the image, adding "[Frangi].tif"
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# For example, the output for "OP_1.tif" would be named "OP_1[Frangi].tif"
100+
l = len(f)
101+
el = len(extension)
102+
output_filepath = f[0:l - el] + "[Tubeness].tif"
103+
saver = ImgSaver(context)
104+
saver.saveImg(output_filepath, output)
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106+
processed += 1
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108+
print('Done. %s file(s) processed. %s file(s) skipped...' % (processed, skipped))
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run()

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