Yuyv format

This topic describes the and bit YUV formats that are recommended for capturing, processing, and displaying video in the Microsoft Windows operating system. These formats use a fixed-point representation for both the luma channel and the chroma C'b and C'r channels.

Precision conversions can be performed using simple bit shifts. The bit representations described here use little-endian WORD values for each channel.

The bit formats also use 16 bits for each channel, with the lowest 6 bits set to zero, as shown in the following diagram. Because the bit and bit representations of the same YUV format have the same memory layout, it is possible to cast a bit representation to a representation with no loss of precision.

It is also possible to cast a bit representation down to a bit representation. The Y and Y formats are an exception to this general rule, however, because they do not share the same memory layout. When the graphics hardware reads a surface that contains a bit representation, it should ignore the low-order 6 bits of each channel.

If a surface contains valid bit data, however, it should be identified as a bit surface. Alpha is assumed to be a linear value that is applied to each component after the component has been converted into its normalized linear form. For images in video memory, the graphics driver selects the memory alignment of the surface. That is, individual lines within a surface are guaranteed to start at a bit boundary, although the alignment can be larger than 32 bits. The origin 0,0 is always the upper-left corner of the surface.

For the purposes of this documentation, the term U is equivalent to Cband the term V is equivalent to Cr. If the format is packed, the first character is 'Y'.

The final two characters in the FOURCC indicate the number of bits per channel, either '16' for 16 bits or '10' for 10 bits. No formats for bit or bit YUV have been defined at this time.

This section describes the memory layout of each format. They share the same memory layout, but P uses 16 bits per channel and P uses 10 bits per channel.

yuyv format

In these two formats, all Y samples appear first in memory as an array of WORD s with an even number of lines. The surface stride can be larger than the width of the Y plane.

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This array is followed immediately by an array of WORD s that contains interleaved U and V samples, as shown in the following diagram. The stride of the combined U-V plane is equal to the stride of the Y plane. The U-V plane has half as many lines as the Y plane.

These two formats are the preferred planar pixel formats for higher precision YUV representations. In these two planar formats, all Y samples appear first in memory as an array of WORD s with an even number of lines.

The U-V plane has the same number of lines as the Y plane. In these two packed formats, each pair of pixels is stored as an array of four WORD s, as shown in the following illustration.

Y is identical to Y except that each sample contains only 10 bits of significant data.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Already on GitHub? Sign in to your account. MartyG-RealSense thanks for the reply. I have tried the same ColorConversionCodes mentioned in the discussion, but still facing the same error. Apologies for the delay in responding further. I was carefully researching your error message. I came across this case with the same error:. However, the input array size after reading the rgb image with input YUYV format contains a single channel 2d array only.

Did you get it through now? Looking forward to your update. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. New issue. Jump to bottom. Copy link Quote reply. This comment has been minimized. Sign in to view. I wonder if you might be able to adapt the conversion information in the link below.

Writable Calibration Parameters for D devices RealSenseCustomerSupport closed this Jan 15, Sign up for free to join this conversation on GitHub. Already have an account?

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yuyv format

Is there a one-stop shop of information that can explain in lamen's terms what each of these settings are meant to do for me when capturing analog video? I keep reading bits and pieces about the use of YUV-based or RGB-based options for different applications like post-processing and filters etcbut it all still has me completely bewildered. If anyone can jot down a quick summary of why I must choose one over the other, that'd help immensely.

Is there any info on this as well? Many thanks. Try the glossary to the left, below What Is. A challange! Quick and clear I'll try. Color - A Color can be represented by a 3 component formula. RGB is an example. Color Space - A color space is really the formula of 3 components used to represent colors. CMY, is another example. RGB24 probably stores the red value, the green value, and the blue value in that order and using 24 bits.

So if the values YUYV were stored in a file, they would cover 2 pixels, and usually this is done in 32 bits. Programs that except 1 probably are ok with the other.

Video camera formats: yuv411, yuv422

YV12 is an example where 4 pixels share a given UV. Let's talk about YUV color in more detail. Y is the black and white info also known as the luma. These are not as easy to visualize as RGB mixing.

Some practical info Most video signals we work with, start as an analog form of YCbCr. This is because to save on transmission bandwidth, they use this weird colorspace. So, using a YUV type colorspace is ok. In fact, because of the built in compression of the chroma, it makes for smaller files and faster processing. Tweaking the luma usually means making the picture sharper.

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Tweaking the chroma usually means making the colors more vibrant. It is easy to overdo both of these. I'll stop there Replies: 40 Last Post: 28th Apr By codemaster in forum Editing. Replies: 8 Last Post: 6th Jan Replies: 8 Last Post: 19th AugThere are a number of video formats used by digital video cameras say, perhaps the most common at the time of writing are called yuv, yuv, and yuv The following briefly describes the layout of the data luminance and two chrominance channels and describes how to convert these to the more familiar RGB, noting of course that it may be more appropriate to do some image processing and analysis in YUV also known as YC b C r space.

The basic idea behind these formats is that the human visual system is less sensitive to high frequency colour information compared to luminance so the colour information can be encoded at a lower spatial resolution.

To reconstruct pixels in pairs, 4 bytes of data are required. The bytes are arranged as u, y1, v, y2. The total number of bytes in the image stream is 2 x width x height. To reconstruct pixels in sets of 4, 6 bytes of data are required.

The bytes are arranged as u, y1, y2, v, y3, y4.

10-bit and 16-bit YUV Video Formats

The plain C source provided here is written for clarify not efficiency, in general these conversions would be performed by the way of lookup tables. Video camera formats: yuv, yuv Written by Paul Bourke August There are a number of video formats used by digital video cameras say, perhaps the most common at the time of writing are called yuv, yuv, and yuvBy using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. The camera outputs images in YUYV format.

yuyv format

I have tried using videoCapture. Both cameras are monochrome, and as far as I can tell the left image information is encoded in the Y channels while the right image is encoded in the U and V channels. For example if I run guvcview I get a single image that contains both the left and right images superpositioned.

It looks like a black and white image the left image is encoded in the Y channels with a green and pink image lying on top the right camera is encoded in the UV channels.

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My goal is to capture the image as YUYV so that I can use split to separate the left image Y channels from the right image U and V channels and display them both as monochrome images. However, once the image has been converted to RGB the luminance and chominance channels get blended together and it becomes impossible to separate the two images. This will allow me to separate the left and right images. OR I need a way of capturing the left and right images separately, but I think this is unlikely.

I dont think there is a way to do this in openCV. I used to wrok on a camera which produce YUV data,and got some problem in configuring it. But this function works for me. In header file videoio. Learn more. Asked 5 years, 4 months ago. Active 3 years ago. Viewed 11k times. I think this would be possible in v4l, but I would prefer to only use openCV if possible.

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Kozuch 1, 3 3 gold badges 19 19 silver badges 35 35 bronze badges. And convert it back to yuv wont work? I have tried converting back to YUV and splitting the channels, but it doesn't quite work.

The image formed from the Y channel is predominantly the left image, but there are still these ghostly shapes left over from the right image and vice versa for the other image. I have a few ideas why that might be. I dont know exactly which one it is, but there are only three channels as opposed to four.

I only found the Y value for each pixel since this should be enough to view just the left image. However there was still a left over ghost image from the right camera. I got the formula here fourcc. Active Oldest Votes.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. The camera outputs images in YUYV format. I have tried using videoCapture. Both cameras are monochrome, and as far as I can tell the left image information is encoded in the Y channels while the right image is encoded in the U and V channels.

For example if I run guvcview I get a single image that contains both the left and right images superpositioned. It looks like a black and white image the left image is encoded in the Y channels with a green and pink image lying on top the right camera is encoded in the UV channels. My goal is to capture the image as YUYV so that I can use split to separate the left image Y channels from the right image U and V channels and display them both as monochrome images.

However, once the image has been converted to RGB the luminance and chominance channels get blended together and it becomes impossible to separate the two images. This will allow me to separate the left and right images.

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OR I need a way of capturing the left and right images separately, but I think this is unlikely. I dont think there is a way to do this in openCV.

I used to wrok on a camera which produce YUV data,and got some problem in configuring it. But this function works for me. In header file videoio. Learn more. Asked 5 years, 3 months ago. Active 3 years ago. Viewed 11k times. I think this would be possible in v4l, but I would prefer to only use openCV if possible. Kozuch 1, 3 3 gold badges 19 19 silver badges 35 35 bronze badges.

And convert it back to yuv wont work? I have tried converting back to YUV and splitting the channels, but it doesn't quite work. The image formed from the Y channel is predominantly the left image, but there are still these ghostly shapes left over from the right image and vice versa for the other image.

I have a few ideas why that might be. I dont know exactly which one it is, but there are only three channels as opposed to four. I only found the Y value for each pixel since this should be enough to view just the left image.

However there was still a left over ghost image from the right camera. I got the formula here fourcc. Active Oldest Votes.I am writing a scaling algorithm for YUV packed format images without any intermediate conversions to RGB or grayscale or what have you.

The questions I have are:. Any references to principles of performing bilinear interpolation on YUYV images would be very helpful! Thanks in advance. You need to use the Mat constructor that takes a pointer to access each component separately, then resize them individually into an appropriately sized buffer.

So to double the size, you need a x buffer. Example code for a doubling of size is below. Note that I'm not sure which is U and which is V, but since you're just resizing, it doesn't matter. You get example code because I had to test it to make sure I wasn't giving you bad advice, and since it's already written And doubling the size of this image horizontally and vertically requires 4x 2x x 2x How do you get x and consequently x? Just use the resize function, and it will interpolate each channel separately.

You plot using the imshow function. Have you looked at the tutorials and documentation? They tell you a lot of this basic stuff. Asked: How to estimate the noiselevel of an image?

How to find a match between 2 shifted hue histograms? How to match two images and find out mistakes. Remove buttons from their background so I can re-use button image. Image stitching from a live video stream. First time here? Check out the FAQ! Hi there! Please sign in help.

YUV Packed format scaling. The questions I have are: I am posting sample data from OpenCV's Mat's pointer to raw data for reference below straight from the memory dump, how do I identify by looking at the data as to what is the Y component and what the UV components are?

That forms my first two YUYV pixels of 32 bits.

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Thanks a lot for your answer. Also, how do you plot this newImg? Oh, I thought you were using the packed YUV. Question Tools Follow. Related questions How to estimate the noiselevel of an image? Copyright OpenCV foundation Powered by Askbot version 0. Please note: OpenCV answers requires javascript to work properly, please enable javascript in your browser, here is how.