I have similar problem like I have seen others have but I don't understand the solutions
I have a USB camera using its own capturing library and the resulting image-frame is stored in a share-memory, lets call it image.
So, I have an image object that I would like to further process with gstreamer.
So how can I do that. My knowledge ends at cv2.VideoCapture(gstr) (Which I do not use in this case since I have the image already. What I need to do is take an image I already have in the application and put into Gstreamer buffer somehow.
What I understand you can create an Appsrc name=source... and then I can take the image into a buffer.
All in all I jstu want to go from an Image array and be able to send it to a Gstream UDP sink and a multifilesink.
I hope it makes sense. I start with Basler USB3 camera and use their api to capture images, hence the confusion.
Related
My goal is to blur the picture a bit using a bilinear debayer.
This is to embody the dirty image of the VHS days.
As a graphic major, I tried to reproduce it with various graphic tools, but did not get the desired quality result.
I want that subtle feeling of faded haze when scanned with a scanner.
I decided to emulate a camera sensor.
The process I envisioned is this:
I convert the tiff,targa.png.jpg format image I made into a bayer format image. I want to restore the original image by debayering it again with a bilinear algorithm.
The reason for the bilinear method is that it degrades most gently and strongly.
The link below is the image change according to the algorithm.
https://www.dpreview.com/forums/post/63514167
I'm not a programmer at all, but I've tried something on my own to get what I want.
https://codegolf.stackexchange.com/questions/86410/reverse-bayer-filter-of-an-image
I succeeded in making an image of the Bayer pattern using the coding here.
And I tried debayering by running the debayer source code downloaded from other places, but it failed because the extension was not supported.
So you can change demoasic(debayer) in various ways
I got a program called darkable and raw therapy and tried to convert it, but these programs could only recognize raw files.
Even the algorithms provided by both programs were so good that it was hard to get the impression that the image was degraded.
How do I make what I want?
What can I look for? I really want to make this.
Please let me know which way I should go.
Is there any predefined code for this or I have to write my own code?
Also, I do not have the camera properties for this, I have only the image taken in fisheye lens and now I have to flatten the images
OpenCV provides a module for working with fisheye images: https://docs.opencv.org/3.4/db/d58/group__calib3d__fisheye.html
This is a tutorial with an example application.
Keep in mind that your task might be a bit hard to achieve since the problem is under-determined. If you have some cues in the image (such as straight lines), that might help. Otherwise, you should seek a way of getting more information about the lens. If it's a known lens type, you might find calibration info online. Also, some images might have the lens used to capture them in the EXIF data.
So, I have a PNG image file like the following example, and I need it to be converted into PGM format.
I'm using Ubuntu and Python, so any of terminal or Python tools would suit just fine. And there sure is a plenty of ways to do this: using ImageMagick convert command or pngtopam package or Python PIL library, etc.
But the point is, the quality of the image is essential in my case, and all of those failed in keeping it, always ending up with:
No need to mention this is totally not what I want to see. And the interesting thing is that when I tried to convert the same image into PGM manually using GIMP, it turned out quite well, looking exactly the way I'd like it to, i.e. the same as the PNG one.
So, that means it is possible to get a PGM image in fine quality after all, and now I'd really appreciate if someone can tell me how do I do that using terminal/Python tools. I guess, there should be some ImageMagick option that does the trick, it's just that I'm not aware of any.
You lost the antialiasing, which is conveyed via the alpha channel. To preserve it, use:
convert in.png -flatten out.pgm
Without -flatten, convert simply deletes the alpha channel; with -flatten it composites the input image against the background color, which is white by default.
Here are the results, magnified 10x so you can see what's going on:
Not flattened:
Flattened:
I need to perform the following operations in my python+django project:
joining videos with same size and bitrate
joining videos and images (for the image manipulation I'll use PIL: writing text to an existing image)
fading in the transitions between videos
I already know of some video editing libraries for python: MLT framework (too complex for my needs), pygame and pymedia (don't include all the features I want), gstreamer bindings (terrible documentation).
I could also do all the work from command line, using ffmpeg, mencoder or transcode.
What's the best approach to do such a thing on a Linux machine.
EDIT: eventually I've chosen to work with melt (mlt's command line)
http://avisynth.org/mediawiki/Main_Page is a scripting language for video.
Because ffmpeg is available on GNU/Linux, i thing using it with modules such as pexpect or subprocess is the best solution....
You can use OpenCV for joining videos and images. See the documentation, in particular the image/video I/O functions.
However, I'm not sure if the library has functions that will do the fading for you.
What codec are you using?
There are two ways to compress video: lossy and lossless. It's easy to tell them apart. Depending on their length, lossy video files are in the megabyte range, lossless (including uncompressed) are in the gigabyte range.
Here's an oversimplification. Editing video files is a lot different from editing film, where you just glue the pieces of film together. It's not just about bitrate, frame rate and resolution. Most lossy video codecs (MPEG 1-4, Ogg Theora, H.26x, VC-1, etc.) start out with a full frame then record only the changes in movement. When you watch the video what you're actually seeing is a static scene with layer after layer of changes pasted on top of it. It looks like you're seeing full frame after full frame, but if you looked at the data in the file all you'd see would be a black background and scrambled blocks of video.
If it's uncompressed or uses a lossless codec (HuffYUV, Lagarith, FFV1, etc.) then you can edit your video file just like film. You still have to re-encode the video but it won't effect video quality and you can cut, copy and paste however you like as long as the resolution and frame rate are the same. If you're video is lossy you have to re-encode it with some loss of video quality, just like saving the same image in JPEG, over and over.
Another option might be to put several pieces of video into a container like MKV and use chapters to have it jump from piece to piece. I seem to remember being told this is possible but I've never tried it so maybe it isn't.
I am wondering if anyone has any experience with Python and video processing. Essentially, I would like to know if there are any libraries that would allow me to do scene detection in a video? If not, are there any that can allow me to split the video up into a series of frames and let me mess about with the pixels?
Thanks!
OpenCV has Python bindings; I don't think it has any scene boundary algorithms / functions built it, but you can definitely use it to write your own.
You can use FFmpeg to do the scene detection and obtain the change frames and their timestamps. The command can be combined with a python script and you can modify it according to your use case.
You can simply use the command:
ffmpeg inputvideo.mp4 -filter_complex "select='gt(scene,0.3)',metadata=print:file=time.txt" -vsync vfr img%03d.png
This will save just the relevant information in the time.txt file like below and also save the shot change images in order:
frame:0 pts:108859 pts_time:1.20954
lavfi.scene_score=0.436456
frame:1 pts:285285 pts_time:3.16983
lavfi.scene_score=0.444537
frame:2 pts:487987 pts_time:5.42208
lavfi.scene_score=0.494256
frame:3 pts:904654 pts_time:10.0517
lavfi.scene_score=0.462327
frame:4 pts:2533781 pts_time:28.1531
lavfi.scene_score=0.460413
frame:5 pts:2668916 pts_time:29.6546
lavfi.scene_score=0.432326
The frame is the serial number of the detected shot change from the starting. Also, choose your threshold value (here 0.3) appropriately for your use case to get correct outputs