Image Comparison: imf_diff
The image comparison utility for mental ray. It supports all image formats
and types that mental ray generates. It allows to detect and display
differences between two images.
The image compare utility is started as:
imf_diff [options] image1 image2 [outimage [outtype]]
The files image1 and image2 will be compared, and
a comparison summary is printed. If outimage is specified, a
difference image with a histogram is written to outimage.
The file format of this file is specified with outtype if
present, or taken from the file name extension if not.
The following options are supported:
- Ignore alpha channel differences.
- Display the difference image and the histogram in a window, by
starting the imf_disp program. This works with and without
an outimage on the command line.
- Write an output image even if the compared input images match.
Normally the output image is written only if there are
- Show differences in false colors, ranging from irrelevant
differences in blue, through significant differences in green, red,
- -g gamma
- Perform gamma correction with the given gamma factor.
- Print brief help text.
- -m thresh
- Set the threshold in the range 0..255. Component differences
less than this threshold are ignored. The default is 3. The main
purpose is to discard differences introduced by dithering (mental
ray's dither option).
- Do not add a histogram to the displayed or saved output image.
- Magnify the differences such that the largest difference is
white and appears at the right edge of the histogram.
- -t thresh
- The difference in percent that causes imf_diff to
return the return code 1 instead of 0. The default is 1. This is
useful for automated test suites.
- Underlay image1 under the displayed or saved difference
image, at 1/10th brightness. This helps locating differences.
- Print verbose progress messages and version banner.
The most common options are -f -u -d (also known as
fear, uncertainty, and doubt).
The sampling strategy of mental ray implies that the true image is
approximated with appropriately selected samples until the desired image
quality criteria are satisfied. This approach ensures consistent quality
but it does not necessarily create images that are bit-for-bit identical
if rendered under different circumstances, such as different image
task sizes, image task assignments
to threads or machines, different machines or different networks, or
different sampling options. Typically, differences shown in blue in the
color histogram are irrelevant.
Copyright © 1986, 2013