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Our workload consisted of stitching together 16 x resolution images.
This makes these stages good candidates for multithreading. The first stage we will parallelize is feature finding.
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This will cause a race condition and will affect our output. The easiest way to solve this problem is to convert the variables into vectors.
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Next, we will parallelize the warping loop. There is a second for loop directly after the first one. We can move the work from it into the first loop to reduce the number of threads launched.
After these changes we were able to run our workload inan 8 second decrease. This is unnecessary if we know the order that our images go in. We should be able to rewrite the algorithm so that each image is only compared against the adjacent images sequentially.
Note that the images must be imported in sequential order.
Mit ihnen ist eine deutlich komplexere Einrichtung und Konfiguration der virtuellen Maschine möglich. Praktischerweise unterstützt Vagrant zudem die Option, innerhalb einer Vagrantfile-Datei mehrere Provisionierungswege anzugeben, die sich dann nacheinander in der Reihenfolge ihres Auftretens abarbeiten lassen. Im Gegensatz zu früheren Versionen von Vagrant führen die modernen Versionen die Provisionierung nicht mehr bei jedem Lauf des Kommandos vagrant up aus, sondern nur noch direkt nach dem Erstellen der virtuellen Maschine.
Optimizing Parameters There are a number of options available that will change the resolution at which we match and blend our images together. Due to our improved matching algorithm, we increased our tolerance for error and can lower some of these parameters to significantly decrease the amount of work we are doing.
Time is reduced because we are now doing matching and stitching on very small images. This does decrease the amount of distinguishing features that can be found and matched, but due to our improved matching algorithm, we do not need to be as precise.
Removing Unnecessary Work Within the compositing loop, there are two blocks of code that do not need to be repeated because we are using same sized images.
These are for resizing mismatched images and initializing blending. These blocks of code can be moved directly in front of the compositing loop: if! By making this change, we were able to complete our workload ina decrease of 2 seconds.
Binäre optionen habrahabr of Feature Finding We did make one more modification, but the details of implementing this improvement depend on the context of the stitching application.
In binäre optionen habrahabr situation, we were building an application to capture images and then stitch the images immediately after all the images were captured.
If your case is similar to this, it is possible to relocate the feature finding portion of the pipeline into the image acquisition stage of your lebensunterhalt auf binären. To do this, you should run the feature finding algorithm immediately after acquiring the image, then save the data to be ready when needed.
It is binäre optionen habrahabr to turn logging off in the final version of the application.
Conclusion With the popularity of panorama capture applications on mobile platforms it is important to have a fast, open source way to stitch images quickly. By decreasing the stitching time we are able to provide a quality experience to the end user. All of these modifications bring the total stitching time from toan 8.
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This table shows the breakdown: Modification.