We are thinking in tex.stackexchange of the thread Mouse Control of 360 Video in Beamer how to store 3D data in a picture. Stepping size is 10 degrees. In matrix, it means 36x18 projections (=648 individual image files). All of them could be stored in a single image. However, we are not sure how. I can do 2D panorama viewer. I am trying to do the 3D viewer such that the result has a manageable size (so 10 degree step size).
Proposal #1
ASCII example of equispaced font where 9 (=18/2) panorama pictures of the hemisphere on top of each other for each angle; everything can be in one picture
1 2 3 4 ... 34 35 36
___2D-panorama #1__ 1
___2D-panorama #2__ 2
________etc________ 3
___________________ 4
___________________ ...
___________________ 8
___2D-panorama_ #9_ 9
How to Store 3D panorama data in Image?
Related
I currently display 115 (!) different sponsor icons at the bottom of many web pages on my website. They're lazy-loaded, but even so, that's quite a lot.
At present, these icons are loaded separately, and are sized 75x50 (or x2 or x3, depending on the screen of the device).
I'm toying with the idea of making them all into one sprite, rather than 115 separate files. That would mean, instead of lots of tiny little files, I'd have one large PNG or WEBP file instead. The way I'm considering doing it would mean the smallest file would be 8,625 pixels across; and the x3 version would be 25,875 pixels across, which seems like a really very large image (albeit only 225 px high).
Will an image of this pixel size cause a browser to choke?
Is a sprite the right way to achieve a faster-loading page here, or is there something else I should be considering?
115 icons with 75 pixel wide sure will calculate to very wide 8625 pixels image, which is only 50px heigh...
but you don't have to use a low height (50 pixel) very wide (8625 pixel) image.
you can make a suitable rectangular smart size image with grid of icons... say, 12 rows of 10 icons per line...
115 x 10 = 1150 + 50 pixel (5 pixel space between 10 icons) total 1200 pixel wide approx.
50 x 12 = 600 + 120 pixel (5 pixel space between 12 icons) total 720 pixel tall approx.
I'm detecting object from a live feed of a camera. The backend model used is ssd_mobilenet_v2. If I capture a image and feed it to the model for 10 times; everytime I get the bounding box of same size. But when I feed a live video to the model (without changing anything in the frame), with every frame I'm getting bouding box of different size (variation of 4 to 5 pixels when the image resolution is 640x480). The reason which I think behind is that due to tiny variations in the digital camera sensors, no two frames will be 100% the same — some pixels will most certainly have different intensity values 1. In this link the user have used GaussianBlur to average pixel intensities across an 21 x 21 region. Is this the only way to fix this ? Or there any better way to correct this.
I'm using Raspberry camera to get the video feed.
https://www.pyimagesearch.com/2015/05/25/basic-motion-detection-and-tracking-with-python-and-opencv/
This is an algorithm i found on this paper for vehicle detection in satellite images, this algorithm is for a sliding window technique.
I have a basic idea, but I don't understand how the Vehicle locating windows are created, any help would be great, thanks.
HDNN = Hybrid Deep Neural Network
Here's my interpretation.
Divide the image into 32 x 32 pixel images.
Calculate the center of all the images. Collect all of these images.
Divide the image into 48 x 48 pixel images. Repeat step 2. Divide the image into 64 x 64 pixel images. Repeat step 2.
Filter all of the images and feed the images that pass the filter into the HDNN. The images are filtered by
Rotating every image 11 times by: 0, 4.5, 9, ··· , 45 degrees
Shrinking or enlarging the non-rotating images into multi-scalings: 0.8, 0.9, 1.0, 1.1, 1.2, 1.3.
For each generated image, we get four preprocessed images: Gray, Gradient, Gradient1 and Gradient2 (see the (a), (b), (c) of Fig. 1). These preprocessed images are normalized into 48 × 48 size and [0,255] gray range.
All of these rotated, shrunk or enlarged preprocessed images are stored in their database and sent to HDNN.
I am using custom images for tab bar icons and they are showing up pixelated and I can't figure out why. The image is 32x32 and 300ppi. I've tried it on 72 and 150ppi and they are all pixelated. Anyone have a solution?
Thanks,
Adam
You need two images one 32 x 32 pixels and named MyIcon.png and one 64 x 64 pixels named MyIcon#2x.png. You then specify MyIcon in the storyboard and iOS will load the correct image for the current hardware.
The ppi (is a print production concept) has no effect in iOS. iOS measures every thing in Points, the iPhone 5 screen is 320 points x 568 points. Retina screens are 2 pixels x 2 pixels for each point. Non retina screens are 1 pixel = 1 point.
UPDATE:
For the iPhone 6 Plus (iOS 8) you now need a third image MyIcon#3x.png that is 96 x 96 see Image resolution for new iPhone 6 and 6+, #3x support added? for lots of good details.
When I am reading about the resolution of a digital image from the following link http://www.rideau-info.com/photos/whatis.html, I confused at the following Paragraph:
If the field of view is 20 feet across, a 3 megapixel camera will be resolving that view at 102 pixels per foot. If that same shot was taken with an 18 Mp camera it would be resolving that view at 259 pixels per foot, 2.5 times more resolution than a 3 Mp camera.
Here, how come the author is arriving at the conclusion: "102 pixels per foot and 259 pixels"?
A 3MP camera, in that article, is 2048 wide x 1536 high. Think of 2048 pixels across as 2048 boxes laid in a straight line. Now, if you were to divide these equally amongst 20 sections (20 feet of field of view), you would get ~120 boxes per section. Hence, the logic behind 102 pixels per foot. Similar reasoning is used for the 18MP camera which is 5184 W x 3546 H. 5184 divided into 20 is ~259.