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The pirate bay depuis la Belgique
1er avril 2013, par
Mis à jour : Avril 2013
Langue : français
Type : Image
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MediaSPIP v0.2
21 juin 2013, parMediaSPIP 0.2 est la première version de MediaSPIP stable.
Sa date de sortie officielle est le 21 juin 2013 et est annoncée ici.
Le fichier zip ici présent contient uniquement les sources de MediaSPIP en version standalone.
Comme pour la version précédente, il est nécessaire d’installer manuellement l’ensemble des dépendances logicielles sur le serveur.
Si vous souhaitez utiliser cette archive pour une installation en mode ferme, il vous faudra également procéder à d’autres modifications (...) -
Creating farms of unique websites
13 avril 2011, parMediaSPIP platforms can be installed as a farm, with a single "core" hosted on a dedicated server and used by multiple websites.
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Sur d’autres sites (13657)
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Screen capture (video screencast) with FFMPEG with very low FPS
20 septembre 2023, par jesusdaI recently changed PCs, I went from having an Intel Core i5 4460 with integrated graphics card to a Xeon E5 2678 v3 with AMD RADEON RX 550 graphics.


On paper, the new PC is on the order of 3 to 7 times more powerful than the old one and I can attest that this is the case in daily use, video and image editing etc. The advantage of having so many cores and threads available is palpable. In terms of games I haven't tried it because I'm not really a gamer and the few games I use are the typical free ones that come with Debian and some emulators that, honestly, already worked fine with the old PC.


However there is one task that brings me head over heels for its terrible performance : video screen capture.


With my old PC I was able to capture at over 60 fps at full screen while doing any task I needed to record.


Even with my lenovo thinkpad x230 I am able to capture screen at over 80fps with total fluency.


The command I have always used is :


ffmpeg -f x11grab -draw_mouse 1 -framerate 60 -video_size 1920x1200 -i :0.0+1680,0 -qscale 0 -pix_fmt yuv420p -c:v libx264 -preset medium -qp 0 -q:v 1 -s 1920x1200 -f matroska -threads 4 video.mkv


notes :


-video_size 1920x1200 -i :0.0+1680,0 y -s 1920x1200are the dimensions and position of the region to capture (my right monitor).

Notice that I even used
-preset mediumand software encoding, so I got very good quality even with that parameter setting and without ever going below 60 fps.

What happens to me now ?


The equipment is unable to capture more than 20 fps which makes any video invalid, with frame drops and not even reach 30fps, which would be the minimum required.


In addition, it is quite noticeable the decrease in responsiveness of the PC as soon as I launch the command. That is, all that fluidity and smoothness that is appreciated when working normally, disappears and even moving a window from one side to another is rough and stumbling.


I have tried with different parameters of ffmpeg, to capture raw, without encoding.


I have tried saving the resulting video directly to RAM disk in order to avoid the possible bottleneck of writing to disk. It doesn't affect it at all.


So, does anyone have any suggestions as to at least where I can dig further to find a solution to the problem ?


Additional data, in case it helps :


$ → inxi
CPU: 12-Core Intel Xeon E5-2678 v3 (-MT MCP-)
speed/min/max: 1201/1200/3300 MHz Kernel: 5.10.0-0.bpo.4-amd64 x86_64
Up: 1d 6h 55m Mem: 6427.6/32012.4 MiB (20.1%)
Storage: 13.76 TiB (55.9% used) Procs: 433 Shell: bash 5.0.18 inxi: 3.0.32


$ → ffmpeg -v
ffmpeg version 4.1.6 Copyright (c) 2000-2020 the FFmpeg developers
 built with gcc 8 (Debian 8.3.0-6)
 configuration: --disable-decoder=amrnb --disable-decoder=libopenjpeg --disable-libopencv --disable-outdev=sdl2 --disable-podpages --disable-sndio --disable-stripping --enable-libaom --enable-avfilter --enable-avresample --enable-gcrypt --disable-gnutls --enable-openssl --enable-gpl --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libfdk-aac --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libilbc --enable-libkvazaar --enable-libmp3lame --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenh264 --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libtesseract --enable-libtheora --enable-libvidstab --enable-libvo-amrwbenc --enable-libvorbis --enable-libvpx --enable-libx265 --enable-libzimg --enable-libxvid --enable-libzvbi --enable-nonfree --enable-opencl --enable-opengl --enable-postproc --enable-pthreads --enable-shared --enable-version3 --enable-libwebp --incdir=/usr/include/x86_64-linux-gnu --libdir=/usr/lib/x86_64-linux-gnu --prefix=/usr --toolchain=hardened --enable-frei0r --enable-chromaprint --enable-libx264 --enable-libiec61883 --enable-libdc1394 --enable-vaapi --enable-libmfx --enable-libvmaf --disable-altivec --shlibdir=/usr/lib/x86_64-linux-gnu
 libavutil 56. 22.100 / 56. 22.100
 libavcodec 58. 35.100 / 58. 35.100
 libavformat 58. 20.100 / 58. 20.100
 libavdevice 58. 5.100 / 58. 5.100
 libavfilter 7. 40.101 / 7. 40.101
 libavresample 4. 0. 0 / 4. 0. 0
 libswscale 5. 3.100 / 5. 3.100
 libswresample 3. 3.100 / 3. 3.100
 libpostproc 55. 3.100 / 55. 3.100


I have the free amdgpu drivers (not amdgpu-pro), but I activated OpenCL just in case.


I followed this tutorial.


$ → glxinfo | grep OpenGL
OpenGL vendor string: AMD
OpenGL renderer string: Radeon RX550/550 Series (POLARIS12, DRM 3.40.0, 5.10.0-0.bpo.4-amd64, LLVM 11.0.1)
OpenGL core profile version string: 4.6 (Core Profile) Mesa 20.3.4
OpenGL core profile shading language version string: 4.60
OpenGL core profile context flags: (none)
OpenGL core profile profile mask: core profile
OpenGL core profile extensions:
OpenGL version string: 4.6 (Compatibility Profile) Mesa 20.3.4
OpenGL shading language version string: 4.60
OpenGL context flags: (none)
OpenGL profile mask: compatibility profile
OpenGL extensions:
OpenGL ES profile version string: OpenGL ES 3.2 Mesa 20.3.4
OpenGL ES profile shading language version string: OpenGL ES GLSL ES 3.20
OpenGL ES profile extensions:





$ → clinfo
Number of platforms 1
 Platform Name Clover
 Platform Vendor Mesa
 Platform Version OpenCL 1.1 Mesa 20.3.4
 Platform Profile FULL_PROFILE
 Platform Extensions cl_khr_icd
 Platform Extensions function suffix MESA

 Platform Name Clover
Number of devices 1
 Device Name Radeon RX550/550 Series (POLARIS12, DRM 3.40.0, 5.10.0-0.bpo.4-amd64, LLVM 11.0.1)
 Device Vendor AMD
 Device Vendor ID 0x1002
 Device Version OpenCL 1.1 Mesa 20.3.4
 Driver Version 20.3.4
 Device OpenCL C Version OpenCL C 1.1
 Device Type GPU
 Device Profile FULL_PROFILE
 Device Available Yes
 Compiler Available Yes
 Max compute units 8
 Max clock frequency 1183MHz
 Max work item dimensions 3
 Max work item sizes 256x256x256
 Max work group size 256
 Preferred work group size multiple 64
 Preferred / native vector sizes
 char 16 / 16
 short 8 / 8
 int 4 / 4
 long 2 / 2
 half 0 / 0 (n/a)
 float 4 / 4
 double 2 / 2 (cl_khr_fp64)
 Half-precision Floating-point support (n/a)
 Single-precision Floating-point support (core)
 Denormals No
 Infinity and NANs Yes
 Round to nearest Yes
 Round to zero No
 Round to infinity No
 IEEE754-2008 fused multiply-add No
 Support is emulated in software No
 Correctly-rounded divide and sqrt operations No
 Double-precision Floating-point support (cl_khr_fp64)
 Denormals Yes
 Infinity and NANs Yes
 Round to nearest Yes
 Round to zero Yes
 Round to infinity Yes
 IEEE754-2008 fused multiply-add Yes
 Support is emulated in software No
 Address bits 64, Little-Endian
 Global memory size 3221225472 (3GiB)
 Error Correction support No
 Max memory allocation 1717986918 (1.6GiB)
 Unified memory for Host and Device No
 Minimum alignment for any data type 128 bytes
 Alignment of base address 32768 bits (4096 bytes)
 Global Memory cache type None
 Image support No
 Local memory type Local
 Local memory size 32768 (32KiB)
 Max number of constant args 16
 Max constant buffer size 67108864 (64MiB)
 Max size of kernel argument 1024
 Queue properties
 Out-of-order execution No
 Profiling Yes
 Profiling timer resolution 0ns
 Execution capabilities
 Run OpenCL kernels Yes
 Run native kernels No
 Device Extensions cl_khr_byte_addressable_store cl_khr_global_int32_base_atomics cl_khr_global_int32_extended_atomics cl_khr_local_int32_base_atomics cl_khr_local_int32_extended_atomics cl_khr_int64_base_atomics cl_khr_int64_extended_atomics cl_khr_fp64

NULL platform behavior
 clGetPlatformInfo(NULL, CL_PLATFORM_NAME, ...) Clover
 clGetDeviceIDs(NULL, CL_DEVICE_TYPE_ALL, ...) Success [MESA]
 clCreateContext(NULL, ...) [default] Success [MESA]
 clCreateContextFromType(NULL, CL_DEVICE_TYPE_DEFAULT) Success (1)
 Platform Name Clover
 Device Name Radeon RX550/550 Series (POLARIS12, DRM 3.40.0, 5.10.0-0.bpo.4-amd64, LLVM 11.0.1)
 clCreateContextFromType(NULL, CL_DEVICE_TYPE_CPU) No devices found in platform
 clCreateContextFromType(NULL, CL_DEVICE_TYPE_GPU) Success (1)
 Platform Name Clover
 Device Name Radeon RX550/550 Series (POLARIS12, DRM 3.40.0, 5.10.0-0.bpo.4-amd64, LLVM 11.0.1)
 clCreateContextFromType(NULL, CL_DEVICE_TYPE_ACCELERATOR) No devices found in platform
 clCreateContextFromType(NULL, CL_DEVICE_TYPE_CUSTOM) No devices found in platform
 clCreateContextFromType(NULL, CL_DEVICE_TYPE_ALL) Success (1)
 Platform Name Clover
 Device Name Radeon RX550/550 Series (POLARIS12, DRM 3.40.0, 5.10.0-0.bpo.4-amd64, LLVM 11.0.1)

ICD loader properties
 ICD loader Name OpenCL ICD Loader
 ICD loader Vendor OCL Icd free software
 ICD loader Version 2.2.12
 ICD loader Profile OpenCL 2.2


This would not be a tearing problem, as no tearing is visible when playing videos and the TearFree driver policy is enabled.


$ → xrandr --verbose | grep TearFree
 TearFree: on
 TearFree: on
 TearFree: on


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Can I convert a django video upload from a form using ffmpeg before storing the video ?
5 mai 2014, par GetItDoneI’ve been stuck for weeks trying to use ffmpeg to convert user uploaded videos to flv. I use heroku to host my website, and store my static and media files on amazon S3 with s3boto. The initial video file will upload fine, however when I retrieve the video and run a celery task (in the same view where the initial video file is uploaded), the new file won’t store on S3. I’ve been trying to get this to work for over a month, with no luck, and really no good resources available for learning how to do this, so I figure maybe if I can get the ffmpeg task to run before storing the video I may be able to get it to work. Unfortunately I’m still not a very advanced at python (or django), so I don’t even know if/how this is possible. Anyone have any ideas ? I am willing to use any solution at this point no matter how ugly, as long as it successfully takes video uploads and converts to flv using ffmpeg, with the resulting file being stored on S3. It doesn’t seem that my situation is very common, because no matter where I look, I cannot find a solution that explains what I should be trying to do. Therefore I will be very appreciative of any guidance. Thanks. My relevant code follows :
#models.py
def content_file_name(instance, filename):
ext = filename.split('.')[-1]
new_file_name = "remove%s.%s" % (uuid.uuid4(), ext)
return '/'.join(['videos', instance.teacher.username, new_file_name])
class BroadcastUpload(models.Model):
title = models.CharField(max_length=50, verbose_name=_('Title'))
description = models.TextField(max_length=100, verbose_name=_('Description'))
teacher = models.ForeignKey(User, null=True, blank=True, related_name='teacher')
created_date = models.DateTimeField(auto_now_add=True)
video_upload = models.FileField(upload_to=content_file_name)
flvfilename = models.CharField(max_length=100, null=True, blank=True)
videothumbnail = models.CharField(max_length=100, null=True, blank=True)
#tasks.py
@task(name='celeryfiles.tasks.convert_flv')
def convert_flv(video_id):
video = BroadcastUpload.objects.get(pk=video_id)
print "ID: %s" % video.id
id = video.id
print "VIDEO NAME: %s" % video.video_upload.name
teacher = video.teacher
print "TEACHER: %s" % teacher
filename = video.video_upload
sourcefile = "%s%s" % (settings.MEDIA_URL, filename)
vidfilename = "%s_%s.flv" % (teacher, video.id)
targetfile = "%svideos/flv/%s" % (settings.MEDIA_URL, vidfilename)
ffmpeg = "ffmpeg -i %s %s" % (sourcefile, vidfilename)
try:
ffmpegresult = subprocess.call(ffmpeg)
#also tried separately with following line:
#ffmpegresult = commands.getoutput(ffmpeg)
print "---------------FFMPEG---------------"
print "FFMPEGRESULT: %s" % ffmpegresult
except Exception as e:
ffmpegresult = None
print("Failed to convert video file %s to %s" % (sourcefile, targetfile))
print(traceback.format_exc())
video.flvfilename = vidfilename
video.save()
@task(name='celeryfiles.tasks.ffmpeg_image')
def ffmpeg_image(video_id):
video = BroadcastUpload.objects.get(pk=video_id)
print "ID: %s" %video.id
id = video.id
print "VIDEO NAME: %s" % video.video_upload.name
teacher = video.teacher
print "TEACHER: %s" % teacher
filename = video.video_upload
sourcefile = "%s%s" % (settings.MEDIA_URL, filename)
imagefilename = "%s_%s.png" % (teacher, video.id)
thumbnailfilename = "%svideos/flv/%s" % (settings.MEDIA_URL, thumbnailfilename)
grabimage = "ffmpeg -y -i %s -vframes 1 -ss 00:00:02 -an -vcodec png -f rawvideo -s 320x240 %s" % (sourcefile, thumbnailfilename)
try:
videothumbnail = subprocess.call(grabimage)
#also tried separately following line:
#videothumbnail = commands.getoutput(grabimage)
print "---------------IMAGE---------------"
print "VIDEOTHUMBNAIL: %s" % videothumbnail
except Exception as e:
videothumbnail = None
print("Failed to convert video file %s to %s" % (sourcefile, thumbnailfilename))
print(traceback.format_exc())
video.videothumbnail = imagefilename
video.save()
#views.py
def upload_broadcast(request):
if request.method == 'POST':
form = BroadcastUploadForm(request.POST, request.FILES)
if form.is_valid():
upload=form.save()
video_id = upload.id
image_grab = ffmpeg_image.delay(video_id)
video_conversion = convert_flv.delay(video_id)
return HttpResponseRedirect('/current_classes/')
else:
form = BroadcastUploadForm(initial={'teacher': request.user,})
return render_to_response('videos/create_video.html', {'form': form,}, context_instance=RequestContext(request))
#settings.py
DEFAULT_FILE_STORAGE = 'myapp.s3utils.MediaRootS3BotoStorage'
DEFAULT_S3_PATH = "media"
STATICFILES_STORAGE = 'myapp.s3utils.StaticRootS3BotoStorage'
STATIC_S3_PATH = "static"
AWS_STORAGE_BUCKET_NAME = 'my_bucket'
CLOUDFRONT_DOMAIN = 'domain.cloudfront.net'
AWS_ACCESS_KEY_ID = 'MY_KEY_ID'
AWS_SECRET_ACCESS_KEY = 'MY_SECRET_KEY'
MEDIA_ROOT = '/%s/' % DEFAULT_S3_PATH
MEDIA_URL = 'http://%s/%s/' % (CLOUDFRONT_DOMAIN, DEFAULT_S3_PATH)
...
#s3utils.py
from storages.backends.s3boto import S3BotoStorage
from django.utils.functional import SimpleLazyObject
StaticRootS3BotoStorage = lambda: S3BotoStorage(location='static')
MediaRootS3BotoStorage = lambda: S3BotoStorage(location='media')I can add any other info if needed to help me solve my problem.
-
Record video stream in rust
19 novembre 2024, par El_LocoI have bought a stereo camera with global shutter and a frame rate of at most 120 fps. https://www.amazon.com/dp/B0D8T3ZSL4?ref_=pe_386300_442618370_TE_sc_as_ri_0#


My next step is to write a program that can show and record a video with desired fps and resolution.


use opencv::{
 core, highgui,
 prelude::*,
 videoio::{self, VideoCapture},
 Result,
};

fn open_camera() -> Result<videocapture> {
 let capture = videoio::VideoCapture::new(2, videoio::CAP_ANY)?;
 return Ok(capture);
}
fn main() -> Result<()> {
 let window = "video capture";
 highgui::named_window(window, highgui::WINDOW_AUTOSIZE)?;
 let mut cam = open_camera()?;
 let opened = videoio::VideoCapture::is_opened(&cam)?;
 if !opened {
 panic!("Unable to open default camera!");
 }
 let width = 3200.0;
 let height = 1200.0;
 cam.set(videoio::CAP_PROP_FRAME_WIDTH, width)?;
 cam.set(videoio::CAP_PROP_FRAME_HEIGHT, height)?;

 // Set the frame rate (FPS)
 let fps = 60.0;
 
 let fourcc = videoio::VideoWriter::fourcc('M', 'J', 'P', 'G')?;
 let mut writer = videoio::VideoWriter::new(
 "video_output.avi",
 fourcc,
 fps,
 core::Size::new(width as i32, height as i32),
 true,
 )?;

 if !writer.is_opened()? {
 println!("Error: Could not open the video writer.");
 }

 let mut frame = core::Mat::default();
 let mut ctr = 0;
 while cam.read(&mut frame)? {
 if frame.empty() {
 break;
 }
 writer.write(&frame)?;
 highgui::imshow(window, &frame)?;
 
 let key = highgui::wait_key(1)?;
 if key > 0 {
 break;
 }
 ctr += 1;
 if ctr == 600 {
 break;
 }
 }
 cam.release()?;
 writer.release()?;
 Ok(())
}
</videocapture>

When I run this code the frame rate is terrible. Like 1 fps or something. For debugging I tried to run in cheese. There I got 30 fps with full resolution
3200x1200. But I cannot change the fps to 60 fps what I can see.

Then I tried to capture a video using ffmpeg :


ffmpeg -f v4l2 -framerate 60 -video_size 3200x1200 -i /dev/video2 output.mp4

With the following output :


[video4linux2,v4l2 @ 0x5a72cbbd1400] The driver changed the time per frame from 1/60 to 1/2
Input #0, video4linux2,v4l2, from '/dev/video2':
 Duration: N/A, start: 2744.250608, bitrate: 122880 kb/s
 Stream #0:0: Video: rawvideo (YUY2 / 0x32595559), yuyv422, 3200x1200, 122880 kb/s, 2 fps, 2 tbr, 1000k tbn
File 'output.mp4' already exists. Overwrite? [y/N]


The frame rate is lowered to 2 fps.


Then I tried to run
v4l2-ctl --list-formats-ext -d 2with the following output :

ioctl: VIDIOC_ENUM_FMT
 Type: Video Capture

 [0]: 'MJPG' (Motion-JPEG, compressed)
 Size: Discrete 3200x1200
 Interval: Discrete 0.017s (60.000 fps)
 Interval: Discrete 0.033s (30.000 fps)
 Interval: Discrete 0.040s (25.000 fps)
 Interval: Discrete 0.050s (20.000 fps)
 Interval: Discrete 0.067s (15.000 fps)
 Interval: Discrete 0.100s (10.000 fps)
 Size: Discrete 2560x720
 Interval: Discrete 0.017s (60.000 fps)
 Interval: Discrete 0.033s (30.000 fps)
 Interval: Discrete 0.040s (25.000 fps)
 Interval: Discrete 0.050s (20.000 fps)
 Interval: Discrete 0.067s (15.000 fps)
 Interval: Discrete 0.100s (10.000 fps)
 Size: Discrete 1600x600
 Interval: Discrete 0.008s (120.000 fps)
 Interval: Discrete 0.017s (60.000 fps)
 Interval: Discrete 0.033s (30.000 fps)
 Interval: Discrete 0.040s (25.000 fps)
 Interval: Discrete 0.050s (20.000 fps)
 Interval: Discrete 0.067s (15.000 fps)


I then tried to open the camera using
qv4land there it seemed to work. Does not seem like I can record a video though.

I am using Rust to learn. I want to be able to programmatically be able to record a video somehow and then do computer vision. The easiest would be to do it in Rust. But other solutions are ok.


Edit
I have found some more this morning :


v4l2-ctl -d 2 --list-formats-ext
ioctl: VIDIOC_ENUM_FMT
 Type: Video Capture

 [0]: 'MJPG' (Motion-JPEG, compressed)
 Size: Discrete 3200x1200
 Interval: Discrete 0.017s (60.000 fps)
 Interval: Discrete 0.033s (30.000 fps)
 Interval: Discrete 0.040s (25.000 fps)
 Interval: Discrete 0.050s (20.000 fps)
 Interval: Discrete 0.067s (15.000 fps)
 Interval: Discrete 0.100s (10.000 fps)

 [1]: 'YUYV' (YUYV 4:2:2)
 Size: Discrete 3200x1200
 Interval: Discrete 0.500s (2.000 fps)
 Size: Discrete 2560x720
 Interval: Discrete 0.500s (2.000 fps)
 Size: Discrete 1600x600
 Interval: Discrete 0.100s (10.000 fps)


I also found here that order of flags was important for
ffmpeg. Running this I can actually record a video with 60 fps :

ffmpeg -framerate 60 -f v4l2 -video_size 3200x1200 -input_format mjpeg -i /dev/video2 output.avi

A drawback is that the images does not look very sharp. You can clearly see the pixels. (I am new to video formats etc as well. Before it has just worked.)


If I change from
avitomkvit is slow again.

In the link above I also saw a suggestion to first do :


ffmpeg -framerate 60 -f v4l2 -video_size 3200x1200 -input_format mjpeg -i /dev/video2 -c copy mjpeg.mkv

and then :


ffmpeg -i mjpeg.mkv -c:v libx264 -crf 23 -preset medium -pix_fmt yuv420p out.mkv

which worked. But I am not sure those flags are ideal for the camera I have. I think it is a good start to make it run as expected using command line and ffmpeg. So I know what format to use and that it actually works as intended before doing it programmatically.


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