# Filtering, normal and distributed mode¶

This example demonstrates Gaussian filtering of an image in normal mode and in distributed mode. For demonstration, the max_memory setting of the distributed mode is set such that we need two jobs even for the small test image used in the example. The filtering result is calculated also in normal mode, and the results between the normal mode and the distributed mode are compared.

The example generalizes easily to other filtering methods like vawe, bilateral, maximum, opening, closing, etc.

def filtering():
"""
Calculates Gaussian blur of image normally and using distributed processing.
Calculates difference of the two versions.
The example generalizes to any available filtering procedure like
vawefilter, bilateralfilter, maxfilter, openingfilter, etc.
"""

# Gaussian filtering (local sequential 'distributed' processing)
# --------------------------------------------------------------

# Enable distributed mode
pi.distribute(Distributor.LOCAL)

# For demonstration, set memory per one job to low value.
# 25 megabytes results in 2 jobs for the default input image in this example.
# Typically you would set this value in local_config.txt file.
pi.maxmemory(25)

# Create output image
filtered = pi.newlike(img)

# Filter
pi.gaussfilter(img, filtered, 5)

# Write output to disk.
# The distributed mode saves internal temporary images as .raw files or .png
# sequences. Writeraw command in distributed mode therefore often converts
# into a simple file rename.

# Disable distributed mode
pi.maxmemory(0) # Sets max memory to automatically determined value
pi.distribute(Distributor.NONE)

# Gaussian filtering (local processing)
# -------------------------------------

# This code is the same than in distributed case above, but without
# pi.distribute-commands.
filtered = pi.newlike(img)
pi.gaussfilter(img, filtered, 5)

# Calculate difference of results of normal and distributed processing
# --------------------------------------------------------------------

# Convert them to float32 so that negative values can be represented, too.
pi.convert(img, ImageDataType.FLOAT32)
pi.convert(img2, ImageDataType.FLOAT32)

# Subtract img2 from img
pi.subtract(img, img2)
pi.writeraw(img, output_file('gauss_difference'))

# Calculate absolute value of each pixel
pi.abs(img)

# Calculate maximal value and place it to image M.
# M will be a 1x1x1 image.
M = pi.newimage(ImageDataType.FLOAT32)
pi.maxval(img, M)

# Get the value of the first pixel of image M.
# In this case M is a 1x1x1 image so we have only one pixel anyway.
M = M.get_value()
print(f"Maximal difference = {M}")


Output:

Enabling distributed computing mode using local sequential processing.
Using 13.51 GiB RAM per task.
Using 25 MiB RAM per task.
Submitting 2 jobs, each estimated to require at most 21 MiB of RAM...
-- Individual job progress information is cut away for clarity --
Waiting for jobs to finish...
Using 13.51 GiB RAM per task.
Distributed computing mode is disabled.
Maximal difference = 0.0