drm | Direct Rendering Manager in pure Go | Graphics library
kandi X-RAY | drm Summary
kandi X-RAY | drm Summary
The Direct Rendering Manager (DRM) is a kernel framework to manage Graphical Processing Units (GPU). It's a kernel abstractions to GPU drivers and a userspace API designed to support the needs of complex graphics devices. DRM was first implemented on Linux but ported to FreeBSD, NetBSD and Solaris (others?). It's the lower level interface between opengl and the graphics card. With this Go library, theoretically, now it's possible to create a X server or a pure OpenGL library in Go (no bindings).
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Top functions reviewed by kandi - BETA
- findCrtc finds an encoders matching the connector
- GetConnector gets a connector .
- Main entry point .
- GetVersion reads version information from file
- GetResources retrieves system resources from a file .
- draw generates a random number of mset data .
- createFramebuffer creates a new framebuffer .
- ListDevices returns a list of all available devices .
- destroyFramebuffer destroys a framebuffer
- GetCrtc retrieves a row from a file .
drm Key Features
drm Examples and Code Snippets
Community Discussions
Trending Discussions on drm
QUESTION
i have the following scenario that is driving me crazy:
i have a capture device. Here the ffprobe on it:
...ANSWER
Answered 2022-Apr-08 at 00:16*.mjpeg
is a raw stream format. FFmpeg documentation states of raw muxers: "They do not store timestamps or metadata." So, instead try storing the data in an mp4 container:
QUESTION
I am trying to upgrade an Azure Media Services v2 implementation to v3 which uses a Custom Key Delivery Service mentioned here to v3. Other than this section and samples from the REST API section, I have found this to be largely undocumented but I believe I have figured out mostly everything, however on the actual Delivery of the Key I am wondering if the format I am returning is in the correct format (I cannot find this easily anywhere). It seems that the Video Player is not working even though I see the request come through and getting sent back out.
Do I return the key in ByteArray format as seen in my v3 example below?
Media Services v2 Implementation
...ANSWER
Answered 2022-Apr-04 at 19:06Many thanks to @Sanjid above, here is the working solution (tested with a Client Side Video Player).
QUESTION
I have installed nvidia-driver-390 after adding a GTX 560 Ti on an Intel Core i5 12600K PC running Kubuntu 20.04 LTS.
After rebooting I get the following error:
...ANSWER
Answered 2021-Dec-13 at 16:51I found the solution and now Kubuntu starts up normally as expected.
First, I purged all drivers and libraries
QUESTION
I have server with centos 7.9 3.10.0-1160.53.1.el7.x86_64
When running my dpdk 19 muliple process application i have softlockup
The server i run on have 2 ixgbe 10G, and one 100G connectx-5
...ANSWER
Answered 2022-Apr-03 at 08:42Adding iommu=pt intel_iommu=on to the grub solve the softlockup and the 10 G Rx
QUESTION
I would like some help to adjust the output called adjusted
that I generate. My idea is to optimize somehow to generate faster. Notice that I'm using pivot_longer
, which takes longer. One idea would be to continue using data.table
as I did to generate SPV
. However, I don't know how to do that in this case for adjusted
. Can you help me?
I would like to generate the same output table as in the question.
...ANSWER
Answered 2022-Mar-31 at 22:22With data.table
, use melt
after doing the filtering and grouping
QUESTION
I would like to use another resolution approach that is faster to calculate the SPV
. See that I use inner_join
, which is a function that takes considerable computational time, so there is another way to calculate the SPV
, other than inner_join
and make it faster.?
ANSWER
Answered 2022-Mar-31 at 17:07As we are using data.table
, data.table
join could be faster
QUESTION
I get m3u8 or mpd file from server and i download it in my app storage. How will i play it offline. Im newbie to offline videos. Any suggestion for its usage methods. Currently i play m3u8 or mpd files with exoplayer which is Dash, HLS or progressive videos. How to play these videos offline by keeping allrights drm in mind.
...ANSWER
Answered 2021-Sep-17 at 16:11The mpd or m3u8 files are just manifests or index files. They are text based containing information about the video and links to the media, i.e. the audio, video, subtitles etc streams.
If you want to download a HLS or DASH stream for offline playback, you will need to download the manifest and the media streams they refer to.
ExoPlayer provides functionality to help you download video and take care of much of this complexity, including specific information on downloading HLS and DASH videos - see the ExoPlayer documentation:
Note the section around tracks selection and bit rates - typically you will want to choose a particular bitrate to download rather than downloading all available ones:
For streaming playbacks, a track selector can be used to choose which of the tracks are played. Similarly, for downloading, a DownloadHelper can be used to choose which of the tracks are downloaded.
To support encrypted streams you need to ensure that the DRM server supports persistent licenses for that content and the entitlements allow offline playback - you will generally need to speak to your DRM provider to confirm this.
QUESTION
Sample code:
...ANSWER
Answered 2022-Jan-14 at 23:05is it allowed at all to use
NAN
andINFINITY
under non-default rounding modes, such asFE_TOWARDZERO
andFE_DOWNWARD
?
The language specification does not express any limitation on using INFINITY
. A conforming implementation must therefore accept it and interpret it consistently with the provisions of the language specification regardless of rounding mode. Specifically, it
expands to a constant expression of type
float
representing positive or unsigned infinity, if available; else to a positive constant of typefloat
that overflows at translation time.
(C17 7.12/4)
That is not conditioned on the rounding mode in effect at any particular time. Since MSVC does have an available float
representation for infinity and FLT_MAX
is required to be a representation of a finite number, it is non-conforming for INFINITY
to evaluate equal to FLT_MAX
in MSVC, under any circumstances. Indeed, MSVC is non-conforming to whatever extent INFINITY
ever fails to evaluate to a positive float
infinity.
It is even more clear for NAN
. The specification says that it
is defined if and only if the implementation supports quiet NaNs for the
float type
.
In that case,
It expands to a constant expression of type
float
representing a quiet NaN.
(C17 7.12/5)
If NAN
is defined at all then it expands to an expression that evaluates to a quiet NaN. Period. If MSVC provides for circumstances in which it instead expands to 0.0, then MSVC is non-conforming under those circumstances.
It should be acknowledged that most, if not all, C implementations have some non-conformity, whether by default or accessible via command-line options or similar. Nevertheless, MSVC is notorious for its comparatively poor conformance among the widely used C implementations.
QUESTION
I have a function that basically filters an SPV
row, as you can see below. Note that I do return_coef <- function(df1, idd,dmda, CategoryChosse) {
However, I would not like df1
as an argument of the function, but rather the attributes of the df1
dataset, which in this case is Id
, date1
, Week
, Category
, DR1
and DRM
. How can I adjust this?
ANSWER
Answered 2022-Mar-21 at 14:06I think what you want is to be able to specify arbitrary columns from a dataframe to be used as Id
, date1
, Week
, etc inside return_coef()
, rather than having to use a dataframe that already has those specific column names. If so, you can use the tidyeval embracing operator, {{
.
QUESTION
I have a data frame with calibration curves for multiple samples (signal s as a function of concentration c):
...ANSWER
Answered 2022-Mar-07 at 22:43cal <- data.frame(sample = c(rep("A", 8), rep("B", 8), rep("C", 8)),
c_std = rep(c(0, 1, 5, 10, 25, 50, 100, 200), 3),
s_std = c(40341, 24196, 13403, 6956, 3000, 1507, 312, 12,
40261, 24250, 13537, 6977, 2940, 1465, 304, 12,
40075, 24469, 13696, 7060, 2972, 1487, 307, 12))
obs <- data.frame(sample = c("A", "B", "C"),
s_std = c(1364, 4726, 521))
library(purrr)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(tidyr)
setNames(predict(drc::drm(c_std ~ s_std, fct = drc::LL2.3(), data = cal), obs), obs$sample)
#> A B C
#> 44.78515 18.20520 79.81111
cal %>%
group_by(sample) %>%
nest() %>%
mutate(
model = map(data, ~drc::drm(c_std ~ s_std, fct = drc::LL2.3(), data = .x)),
pred = map2(model, sample, ~predict(.x, filter(obs, sample == .y)))
) %>%
unnest(pred)
#> # A tibble: 3 x 4
#> # Groups: sample [3]
#> sample data model pred
#>
#> 1 A 45.1
#> 2 B 18.1
#> 3 C 79.8
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