I've read a lot of tutorials on how to overlay an image in AviSynth, but wonder if there is a way to place several images on a video at specific time positions. I've been able to render videos with a transparent png logo, but didn't find any tutorial how to place different images at different frame positions.
I believe you have to figure out time positions from the frame rate. For instance the below sample will show the overlay image between 101 - 200 frames (4th to 8th second):
AviSource("sample.avi", false).AssumeFPS(25).ConvertToRGB
img = ImageSource("sample.png")
Trim(0, 100) + Trim(101, 200).Overlay(img, 20, 30, opacity = 0.5) + Trim(201, 0)
Thanks!
Depending on your input codec you might need to replace
AviSource("sample.avi", false).AssumeFPS(25).ConvertToRGB
with
DirectShowSource("sample.avi")
If you use the wrong one you might get a error in the line of AVISource couldn't locate a decompressor for fourcc mjpg
Related
I have a video written with the Xvid MPEG4-Codec. Unfortunately, each frame has an annoying bar to the right and to the bottom of a couple of pixels. What I would like to do is to remove this bar, preferably using MATLAB.
For this purpose, I have written this code to try this out:
function [] = changeVideo(in_path, out_path, reqSize)
videoList = dir(strcat(in_path, '\*.avi'));
for ii = 1:numel(videoList)
vidReader = VideoReader(strcat([in_path '\'], videoList(ii).name));
vidWriter = VideoWriter(strcat([out_path '\'], videoList(ii).name),'MPEG-4');
open(vidWriter);
while hasFrame(vidReader)
% here I would change the size of the frame
writeVideo(vidWriter,readFrame(vidReader));
end
close(vidWriter);
close(vidReader);
end
end
Unfortunately, this does not seem to work. The resulting video has a different memory footprint and quality than the original video. Since MATLAB can read the original video, I was hoping that there is a way to replicate this video, but only change each frame slightly in its size. Is this possible?
Thank you!
I am currently recording on a single camera the images, one aside of the other one, of the same sample out of a microscope.
I have 2 issues with that, and I figured out that in post procesing with Matlab I could arrange these questions.
-First, the 2 images on the camera are supposed to have the same pixel size, or one is just a litle bigger than the other one, probably because of optical pathways. What is the adapted Matlab function or way to correlate the two images so they will have exactly the same pixel size in X and Y ?
Two images on same camera , one bigger or smaller compared to the other one
-Secondly, my sample is moving a litle during the recording ( while still staying in my field of view of course ). To make my analysis easier, it would be suitable that I could correct the images so the sample remain at the same place as in the first image, to perform calculations on it easier. What would be the adapted Matlab function or way to correct this movement in the image ?
Sample moving in the image on the camera
Sorry for the poor quality of my drawings !
Thank you very much for your advices and help.
First zero-pad the images to a sufficient degree, to get them both to double the size of the bigger one.
size_padding = max(size(fig1),size(fig2));
fig1_pad = padarray(fig1,size_padding-size(fig1),'post');
fig2_pad = padarray(fig2,size_padding-size(fig2),'post');
Assuming the sample is the only feature present in the images, the best way to proceed would be to use the xcorr2() function and find the lag corresponding to the maximum correlation, to get the space shift between the two images:
xc = xcorr2(fig1_pad,fig2_pad);
[max_cc, imax] = max(abs(xc(:)));
[ypeak, xpeak] = ind2sub(size(xc),imax(1));
corr_offset = [ (ypeak-size(fig2_pad,1)) (xpeak-size(fig2_pad,2)) ];
You then use circshift() to shift one of the images using the lag you obtained in the last step.
fig2_shift = circshift(fig2_pad,corr_offset);
You now have two images of the same size, where hopefully the sample is in the same position. If you want to remove the padding zeroes, crop the images to your liking with respect to the center using imcrop().
I have a small problem with finding the pixel size of an image. I am to find size of nano and micro particles on my BW image. I used regionprops to get the area - then the diameter. Now i know the value in pixels. How do i convert to micro or nano meter scale? Do I take into account the sensor size(6.5umx6.5um) of my camera?
I use MATLAB for image processing.
Thank you
there is a function called imfinfo which will return a struct. In this struct you will maybe find three fields (it depends on the coder that you used for the image format) called XResolution, YResolution and ResolutionUnit. Using this 3 fields you can easily get pixel size, for example if XResolution=10, YResolution=10 and ResolutionUnit='meter' then you have a 100cm2 pixels (its a bit unreal i know :))
I hope this helps and that your image file contains the XResolution and YResolution information in your header.
I'm a bit of a noob in MATLAB (and image processing in general) and I'm wondering if you can help me with a bit of an issue I'm having. Essentially, I'm given an image of an alley, and then multiple images of the same alley, but with different contrasts and some of the images have a picture of a robber in them. I need to be able to detect the robbers in the images, and run the same code on all of the images (i.e. I'm not allowed to custom-tailor the code for specific images). Here's what I have so far:
background = imread('backalley.jpg');
criminal = imread('backalleyX.jpg'); % Where X is the number of the image, there
%are 16 in total from 0 to 15
J = imhist(background);
K = histeq(criminal,J);
diffImage = abs(double(background)-double(K));
thresholdValue = 103;
filteredImage = diffImage > thresholdValue;
(Keep in mind I'm still playing around with the thresholdValue)
This leaves me with either a gray image if there isn't a robber, or a black and white image showing some of the features of the robber. The issue I'm having is that three of the 16 images with a very high contrast initially leave me with most of the features of the alley still visible, even after having histogram equalization done. Is there anything I can do to filter these images or adjust the contrast better, that won't cause an issue with the rest of the successfully processed images? Unfortunately since I'm new here I can't post images showing what's going on, sorry.
EDIT: Here is a link to the photobucket album: http://s997.photobucket.com/user/52TulaSKS/library/Image%20Processing
All of the images needing processing are there, as well as the original, and examples of processed images. I gave titles to the important ones (original, ones giving me trouble, and the examples of correctly and incorrectly processed images).
Change your threshold to a higher value.
I am working with images of size 2 to 4MB. I am working with images of resolution 1200x1600 by performing scaling, translation and rotation operations. I want to add another image on that and save it to photo album. My app is crashing after i successfully edit one image and save to photos. Its happening because of images size i think. I want to maintain the 90% of resolution of the images.
I am releasing some images when i get memory warning. But still it crashes as i am working with 2 images of size 3MB each and context of size 1200x1600 and getting a image from the context at the same time.
Is there any way to compress images and work with it?
I doubt it. Even compressing and decompressing an image without doing anything to it loses information. I suspect that any algorithms to manipulate compressed images would be hopelessly lossy.
Having said that, it may be technically possible. For instance, rotating a Fourier transform also rotates the original image. But practical image compression isn't usually as simple as just computing a Fourier transform.
Alternatively, you could write piecemeal algorithms that chop the image up into bite-sized pieces, transform the pieces and reassemble them afterwards. You might also provide a real-time view of the process by applying the same transform to a smaller version of the full image.
The key will be never to full decode the entire image into memory at full size.
If you need to display the image, there's no reason to do that at full size -- the display on the iPhone is too small to take advantage of that. For image objects that are for display, decode the image in scaled down form.
For processing, you will need to write custom code that works on a stream of pixels rather than an in-memory array. I don't know if this is available on the iPhone already, but you can write it yourself by writing to the libpng library API directly.
For example, your code right now probably looks something like this (pseudo code)
img = ReadImageFromFile("image.png")
img2 = RotateImage(img, 90)
SaveImage(img2, "image2.png")
The key thing to understand, is that in this case, img is not the data in the PNG file (2MB), but the fully uncompressed image (~6mb). RotateImage (or whatever it's called) returns another image of about this same size. If you are scaling up, it's even worse.
You want code that looks more like this (but there might not be any API's for you to do it -- you might have to write it yourself)
imgPixelGetter = PixelDecoderFromFile("image.png")
imgPixelSaver = OpenImageForAppending("image2.png")
w = imgPixelGetter.Width
h = imgPixelGetter.Height
// set up a 90 degree rotate
imgPixelSaver.Width = h
imgPixelSaver.Height = w
// read each vertical scanline of pixels
for (x = 0; x < w; ++x) {
pixelRect = imgPixelGetter.ReadRect(x, 0, 1, h) // x, y, w, h
pixelRect.Rotate(90); // it's now got a width of h and a height of 1
imgPixelSaver.AppendScanLine(pixelRect)
}
In this algorithm, you never had the entire image in memory at once -- you read it out piece by piece and saved it. You can write similar algorithms for scaling and cropping.
The tradeoff is that it will be slower than just decoding it into memory -- it depends on the image format and the code that's doing the ReadRect(). Unfortunately, PNG is not designed for this kind of access to the pixels.