Hi Joan,
Thanks for the contribution, and sorry for the delayed response. I've only
now found time to digest everything. If you're interested in the
image_pipeline, you should consider joining the Perception Pipelines
SIG<
http://www.ros.org/wiki/fuerte/Planning/Perception%20Pipelines>,
or at least its mailing list.
I created a ticket to track your code and suggestions:
https://code.ros.org/trac/ros-pkg/ticket/5201. Further comments below.
On Thu, Sep 29, 2011 at 10:05 AM, Joan Pau Beltran
<
joanpau.beltran@uib.cat>wrote:
> Electric Emy includes a nodelet to perform crop and binning of images.
> In our research group we find this very useful and were wondering why it
> was missing.
> So very good news to see it there. Congratulations! We hope to see this
> node documented in the image_proc wiki soon.
>
Thanks for the reminder :). I've updated the reference docs.
If the input image encoding is bayer, this nodelet performs crop/binning and
> debayering.
> We would like to discuss with the ROS community if it would be more
> convenient perform only the crop/binning, and do NOT perform the debayering.
> That debayering process, with possibly other image_proc tasks, could be
> performed by another image_proc node in the subsampled image topic
> namespace. From our point of view, it will have two main advantages:
>
> 1. It will allow more complex debayering methods to be aplied to the
> scaled images by image_proc, with zero mantainment cost of the crop/binning
> nodelet.
>
> I have problems with this. If you're doing 2x2 decimation of a Bayer image,
you have all the information to transform a 2x2 Bayer cell into one RGB
pixel right there. The "debayering" is trivial, no interpolation needed
whatsoever, and you lose very little information (just from averaging the 2
green pixels in each cell).
With your approach, you throw away 3/4ths of the information up front, and
hope that a complex (hence expensive) debayering algorithm can fill some of
that back in. You burn a lot more CPU and, no matter how sophisticated your
algorithm, you won't get results as good as the trivial downsample-to-RGB
approach.
>
> 1. It will allow subsampling bayer images to reduce its size and send
> them through the network without losing the color information (if sended in
> mono) nor triplicating the required bandwith (if sended in color)
>
> True, full RGB takes 3x the bandwidth of Bayer. Then again, imposing a
Bayer pattern is a pretty simplistic compression method. We have
image_transport for applications where bandwidth is limited.
>
>
> The subsampling is quite easy to implement. It just changes depending on
> the encoding. Subsampling of mono and color encoded images is quite
> intuitive. In Bayer encodings, the idea is to consider each set of 2x2
> pixels as a color cell, and these color cells are the subsampling units. The
> resulting image has the same encoding than the original one.
>
As implemented using decimation, you'll get bad aliasing artifacts when
downsampling Bayer images, even in the 2x2 case where you have the
information to do it perfectly. For each set of 4 rows/columns you throw out
2 adjacent ones.
Cameras that support "color binning" in hardware do something like this, but
using binning/subsampling (averaging blocks of Bayer cells) instead of
decimation (throwing away all but one). In that case the aliasing is
mitigated.
The attached file crop_decimate.cpp includes the needed modifications to
> implement the explained functionality, also solving some issues and todo's
> in the current implementation:
>
> 1. It should handle correctly 16 bit encodings (and any other bit
> depth), since in that implementation different bit depths do no need
> specific methods. I say it should because I could not test it. I do not have
> any camera or bagfile with 16 bit images.
>
> Nice. Jack O'Quin has cameras that produce 16-bit Bayer, so we can get some
test data for this.
>
> 1. Offsets are checked to be smaller than image size, and an error is
> reported if it is not the case. In contrast, if the demanded ROI is bigger
> than the image (with or height too large for the given the offset and image
> size), it is SILENTLY adjusted. Should this adjustment be warned?
>
> I figured users might be changing the ROI through reconfigure_gui, in which
case warnings would just be annoying. WARN_ONCE might be appropriate. What
the nodelet should do is publish the ROI actually used back through
dynamic_reconfigure, so that the user will at least get GUI feedback.
>
> 1. On Bayer images, not only the offsets are checked to be even, but
> also the resulting image size. This is needed to end up with a well formed
> Bayer image with full color cells.
>
> Good point.
>
>
> The file crop_decimate2.cpp is a slightly different implementation of the
> same idea, but performing the subsampling in just one function for all the
> encodings (given the right parameters). The function takes as targuments the
> cell size (2x2 for Bayer, 1x1 for other encodings, but any other size could
> be used).
>
That's pretty neat. Your approach does look like an improvement over how I
handled mono and RGB-type encodings.
Probably the code is a little more elegant, but it may have a little
> overhead because of the inner loop (probably insignificant).
>
Probably it's fine. If the overhead actually is significant we could
template subsample() on bytes_per_pixel to generate optimized versions.
A part from that, and to present an alternative approach, it also solves the
> inconsistent image - ROI offsets/size in a different way. The output image
> size is always the same than the given ROI once subsampled, and the region
> that falls out of the image is zeroed. This is similar to what happens to
> the rectified image when a zero alpha value is set in the calibration.
>
I prefer the first approach, changing the ROI to fit. Using zero to denote
invalid or out-of-range pixels is ambiguous, since zero is a valid pixel
value. This is also a problem with how we do rectified images with alpha <
1.
It should be said that this subsampling is the crudest one, and it can
> introduce artifacts in the resulting image. Another completely different
> approach will be to use OpenCV resizing functions to do the job. In that
> case, as in the debayering process, a set of methods could be supported with
> an extra dynamic_reconfigure parameter.
>
That's true. The current decimation approach is basically equivalent to
cv::resize using the INTER_NEAREST (nearest-neighbor) method, which is fast
but indeed crude. We could get much better output from INTER_AREA, which
gives Moire-free results. A parameter to switch between at least those two
options would be useful.
I think that last OpenCV versions only understand images as bgr8 encoded
> cv::Mat's (even mono images), so the approach will be:
>
> 1. subscribe to the color image
> 2. subsample it to a color scaled image
> 3. from this color scaled image build the mono scaled image if
> necessary
>
> And it won't be possible to scale a bayer image, debayer it first will be
> mandatory. Is there any OpenCV expert that can confirm or refutate these
> statements? Any information about the resizing functions will be
> appreciated, since the documentation is quite poor.
>
The very latest documentation for cv::resize is
here<
http://opencv.itseez.com/modules/imgproc/doc/geometric_transformations.html#cv.Resize>and
seems adequate to me. I believe it handles one-channel and
three-channel
8- or 16-bit images just fine.
For Bayer, I would still do the 2x2 decimation to RGB (almost lossless), and
then use cv::resize for any further decimation.
Cheers,
Patrick
(text/plain)