gallinazo.flightgear.orgGallinazo – Aviation and technology ramblings

Skip to content Gallinazo Aviation and technology ramblings Model Airplanes Model Airplanes Current Current Aerosky 185 Dynam Smart Trainer Polaris Ultra Rascal C Seawind Shrike Sonic 64 Needs Work Needs Work Citabria Mariner PBY Catalina Retired Retired Pre R/C Ace High Mk-II Big Stick Aerobird Challenger Fairchild F-24 Four Star 40 Kadet Mk I Kadet Mk II MiG-15 P-47 Piper Cub Super Sportster Mk-I Sweet Stik Tiger Bipe 40 Ultra Sport UAS UAS ArduPilot Kadet Senior Rascal #1 Rascal #2 Resolution Senior Telemaster Misc Design Software Software Image Analysis Simulation Technology Technology Marine Debris Travel Welcome More Back Simple Vignette Correction From wikipedia: In photography and optics, vignetting is a reduction of an image’s brightness or saturation toward the periphery compared to the image center. When presenting a collection of images as a mosaic, the vignetting in the imagery can cause visual discontinuities at the image borders. Here I present a simple strategy to model and correct the vignette in a collection of images. Step 1: Compute the pixel-wise average for a set of images I start by creating 2d numpy array of float32 type with the same dimension as our camera. Each [u, v] position in the numpy array is the sum of that pixel position from each image. The “average image” is created by dividing each pixel sum by the total number of images. Here is an average of 7 images. Features from the individual images show through, but you can already begin to see the darkening at the corners: Now here is the average of the full 1635 image set. The vignetting is clearly visible at the edges and corners. No individual image details are discernible: Step 2: Compute a best fit function The camera/lens calibration provides the optical center of the image (which may be offset from the actual center of the image due to lens irregularities.) For each [u, v] pixel coordinate in the final image, the algorithm computes the radius from the optical center versus pixel intensity. (This is done for each BGR color channel individually.) Here is the plot of radius vs. red channel intensity and the best fit function. The fit function is a * x^4 + b * x^2 + c. Step 3: Generate the idealized vignette correction mask Finally, the algorithm, generates an idealized vignette mask based on the fit function. Dithering is used to hide possible banding artifacts. Here is the final vignette mask. This is summed with the original images to give them an approximate even brightness across the entire image. This may look like a plain black image, but the corners are carefully lightened based on the best fit function determined above. Step 4: Apply the mask to the images Here is a before mosaic with no vignette correction. I intentionally picked a snowy scene shot in low light because the vignetting is maximally visible under these conditions. After correction. Here is the same scene with vignetting correction applied. The result is not perfect, but the improvement is substantial. The code to compute the vignette correction for a set of images and then non-destructively apply the correction for visual presentation (along with code to optimize the fit of the images in the first place) is all part of the ImageAnalysis project developed by the University of Minnesota AEM Department, UAV Lab. The code is written entirely in python and is licensed with the MIT open-source software license: https://github.com/UASLab/ImageAnalysis Posted by curt April 25, 2019 April 25, 2019 Posted in Image Analysis , UAS Leave a comment on Simple Vignette Correction DJI Phantom 4 Pro camera vs. Sony A6000 Recently I flew a DJI Phantom 4 Pro v2 head to head with an in-house (U of MN AEM UAS Lab) developed fixed wing UAS. This comparison isn’t entirely apples to apples, but maybe someone will find it useful. DJI is the king of the hill for small UAS aerial surveys. Once you figure out the apps and a few basic things, operating one of these is pretty much click and fly and makes aerial survey work about as easy as it can be. Some quick details of our system: Camera horizontal field of view: about 67 degrees. 20 Megapixel RGB sensor (4864 x 3648 pixels) At 400′ AGL the image would cover approximately 162 x 121 meters @ 3.3 cm per pixel. Useful mission flight time: approximately 20 minutes. Typical mission cruise speed 13 mph. Megapixels collected per mission: about 8000. Vertical take of and landing for operating in constrained areas: Yes! Our in-house fixed wing survey platform is a full size X-UAV Talon (with the 15 cm wing extensions.) The camera is a Sony A6000 with a 30mm prime lens. Camera horizontal field of view: about 43 degrees. 24 Megapixel RGB sensor (6000 x 4000 pixels) At 400′ AGL the image would cover approximagely 96 x 63 meters @ 1.6 cm per pixel. Useful mission flight time: approximately 75 minutes. Typical mission cruise speed 30 mph. Megapixels collected per mission: about 60,000 (7.5x more data than a DJI flight.) Operates out of constrained areas: No! (But the system has auto-launch and auto-land capabilities to minimize pilot workload during operations.) Side note: our in-house system flies with our in-house “Goldy3” autopilot flight controller. Recently we flew both of these systems over the same area for a head to head match up. We flew both systems at an altitude of 200′ AGL. If you look at the respective camera specs you will already know what to expect, but I wanted to share the head to head imagery. The Sony A6000 yields approximately double the pixel resolution at the same altitude (4x the pixels for any area.) In addition, the Sony imaging sensor has about 3x the area compared to the DJI imaging sensor. You can see much better pixel detail in the Sony images as well as more subtle color variations, less washout (saturation), richer colors, and fewer compression artifacts when you zoom way in. So with all that said, here are the image snippets for direct comparison. Important! For best image pair viewing results: right-click on each image and select “open in new window”. Then drag the windows so you can see the images side by side. You may be able to click in each new window to expand the size to full resolution. If you are reading this on your phone, then I’m sorry! Van: look for subtle things like the windshield washer nozzles, the wheel rim pattern, gravel pattern. Stumps: notice the overall details, you can see bark patterns in the sony image. Evergreens and snow patch: see how the subtle details are preserved in the snow patch better with the Sony camera and individual pine needles are clearly visible. Spring buds: notice the details in the fine branching structures and the new buds on all the branches. This is a pair of images that also show the extra jpg compression artifacts in the DJI images. Gravel road: notice the richer colors in the Sony image, notice the subtle details in the snow patch (the DJI image gets totally washed out in the snow area) and notice the overall sharper detail in the gravel. And again you can see where the extra jpg compression on the DJI image limits how much you can usefully zoom in. Pile of branches: look for the detail in the bark pattern, the shading detail in the snow patches, the detail vs. compression artifacts in the tiny branches. Conclusion: This is a comparison of two systems (the Phantom 4 is popular commercial system vs. our U of MN UAS Lab in-house built fixed wing system) both flying at 200′ AGL. This is not a perfect apples to apples comparison and it may not be representative of your own typical use cases. What I have shown here is that a higher quality camera produces better pictures (duh!) For some types of missions the details and the image quality do matter! If you have a thought or question about the aircraft, cameras, or images, please leave a comment below. Thanks for reading! Posted by curt April 12, 2019 April 12, 2019 Posted in Image Analysis , UAS Leave a comment on DJI Phantom 4 Pro camera vs. Sony A6000 Al...