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• FFMPEG "Could not allocate memory" Errors through php

  10 décembre 2019, par Applepiee

  I’ve tried to find a solution online for days now but cannot find any solution.

  I just switched server (Intel Xeon to AMD) and since the switch I’ve not been able to get ffmpeg conversions working through the php script. ffmpeg was the exact same version and all php settings are set.

  All commands that the script executes (copied from log files) were tried in shell and executed with no problems.

  The errors look like this :

  [124] =>       handler_name    : Video Media Handler
  
      [125] =>     Stream #8:1(eng): Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, stereo, fltp, 160 kb/s (default)
  
      [126] =>     Metadata:
  
      [127] =>       handler_name    : Sound Media Handler
  
      [128] => Stream mapping:
  
      [129] =>   Stream #0:0 (h264) -> concat:in0:v0
  
      [130] =>   Stream #1:0 (h264) -> concat:in1:v0
  
      [131] =>   Stream #2:0 (h264) -> concat:in2:v0
  
      [132] =>   Stream #3:0 (h264) -> concat:in3:v0
  
      [133] =>   Stream #4:0 (h264) -> concat:in4:v0
  
      [134] =>   Stream #5:0 (h264) -> concat:in5:v0
  
      [135] =>   Stream #6:0 (h264) -> concat:in6:v0
  
      [136] =>   Stream #7:0 (h264) -> concat:in7:v0
  
      [137] =>   scale -> Stream #0:0 (libvpx)
  
      [138] => Press [q] to stop, [?] for help
  
      [139] => [h264 @ 0x34f1dc0] get_buffer() failed
  
      [140] => [h264 @ 0x34f1dc0] thread_get_buffer() failed
  
      [141] => [h264 @ 0x34f1dc0] decode_slice_header error
  
      [142] => [h264 @ 0x34f1dc0] no frame!
  
      [143] => [h264 @ 0x350e680] Error splitting the input into NAL units.
  
      [144] => [h264 @ 0x352af40] Cannot allocate memory.
  
      [145] => [h264 @ 0x352af40] Could not allocate memory
  
      [146] => [h264 @ 0x352af40] h264_slice_header_init() failedError while decoding stream #0:0: Cannot allocate memory
  
      [147] => [h264 @ 0x352af40] Cannot allocate memory.
  
      [148] => [h264 @ 0x352af40] Could not allocate memory
  
      [149] => [h264 @ 0x352af40] h264_slice_header_init() failedError while decoding stream #0:0: Cannot allocate memory
  
      [150] => [h264 @ 0x352af40] Cannot allocate memory.
  
      [151] => [h264 @ 0x352af40] Could not allocate memory
  
      [152] => [h264 @ 0x352af40] h264_slice_header_init() failedError while decoding stream #0:0: Cannot allocate memory
  
      [153] => [h264 @ 0x352af40] Cannot allocate memory.
  
      [154] => [h264 @ 0x352af40] Could not allocate memory
  
      [155] => [h264 @ 0x352af40] h264_slice_header_init() failedError while decoding stream #0:0: Cannot allocate memory
  
      [156] => [h264 @ 0x352af40] Cannot allocate memory.
  
      [157] => [h264 @ 0x352af40] Could not allocate memory
  
  ...
  
  ..
  
    [211519] => [h264 @ 0x886a3c0] h264_slice_header_init() failedToo many errors when draining, this is a bug. Stop draining and force EOF.
  
      [211520] => Error while decoding stream #7:0: Internal bug, should not have happened
  
      [211521] => Cannot allocate memory.
  
      [211522] => sws: initFilter failed
  
      [211523] => [Parsed_scale_1 @ 0x8e0be40] Failed to configure output pad on Parsed_scale_1
  
      [211524] => Error reinitializing filters!
  
      [211525] => Error while filtering: Operation not permitted
  
      [211526] => Finishing stream 0:0 without any data written to it.
  
      [211527] => [libvpx @ 0x6f4c600] v1.8.1-301-g89375f031
  
      [211528] => Output #0, webm, to '/home/website/public_html/media/videos/tmb/2420/video_copy.webm':
  
      [211529] =>   Metadata:
  
      [211530] =>     major_brand     : isom
  
      [211531] =>     minor_version   : 512
  
      [211532] =>     compatible_brands: isomiso2avc1mp41
  
      [211533] =>     title           : Aibeya The Animation
  
      [211534] =>     encoder         : Lavf58.35.100
  
      [211535] =>     Chapter #0:0: start 0.000000, end 102.102000
  
      [211536] =>     Metadata:
  
      [211537] =>       title           : Intro
  
      [211538] =>     Chapter #0:1: start 102.102000, end 110.369000
  
      [211539] =>     Metadata:
  
      [211540] =>       title           : Title
  
      [211541] =>     Chapter #0:2: start 110.369000, end 312.179000
  
      [211542] =>     Metadata:
  
      [211543] =>       title           : Part 1
  
      [211544] =>     Chapter #0:3: start 312.179000, end 548.415000
  
      [211545] =>     Metadata:
  
      [211546] =>       title           : Part 2
  
      [211547] =>     Chapter #0:4: start 548.415000, end 706.831000
  
      [211548] =>     Metadata:
  
      [211549] =>       title           : Part 3
  
      [211550] =>     Chapter #0:5: start 706.831000, end 1011.052000
  
      [211551] =>     Metadata:
  
      [211552] =>       title           : Part 4
  
      [211553] =>     Chapter #0:6: start 1011.052000, end 1198.823000
  
      [211554] =>     Metadata:
  
      [211555] =>       title           : Part 5
  
      [211556] =>     Chapter #0:7: start 1198.823000, end 1501.408000
  
      [211557] =>     Metadata:
  
      [211558] =>       title           : Part 6
  
      [211559] =>     Chapter #0:8: start 1501.408000, end 1579.945000
  
      [211560] =>     Metadata:
  
      [211561] =>       title           : Part 7
  
      [211562] =>     Chapter #0:9: start 1579.945000, end 1654.293000
  
      [211563] =>     Metadata:
  
      [211564] =>       title           : Ending
  
      [211565] =>     Stream #0:0: Video: vp8 (libvpx), yuv420p, 400x240 [SAR 837:785 DAR 279:157], q=10-42, 250 kb/s, 23.98 fps, 1k tbn, 23.98 tbc (default)
  
      [211566] =>     Metadata:
  
      [211567] =>       encoder         : Lavc58.64.101 libvpx
  
      [211568] =>     Side data:
  
      [211569] =>       cpb: bitrate max/min/avg: 0/0/0 buffer size: 600000 vbv_delay: N/A
  
      [211570] => frame=    0 fps=0.0 q=0.0 Lsize=       1kB time=00:00:00.00 bitrate=N/A speed=   0x
  
      [211571] => video:0kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: unknown
  
      [211572] => Output file is empty, nothing was encoded (check -ss / -t / -frames parameters if used)
  
      [211573] => Conversion failed!
  
  )

  Command example used :

  ffmpeg_command is /usr/local/bin/ffmpeg -ss 100 -t 1 -i /home/website/public_html/media/videos/iphone/2407.mp4 -ss 197 -t 2 -i /home/website/public_html/media/videos/iphone/2407.mp4 -ss 294 -t 3 -i /home/website/public_html/media/videos/iphone/2407.mp4 -ss 391 -t 3 -i /home/website/public_html/media/videos/iphone/2407.mp4 -ss 488 -t 3 -i /home/website/public_html/media/videos/iphone/2407.mp4 -ss 585 -t 3 -i /home/website/public_html/media/videos/iphone/2407.mp4 -ss 682 -t 3 -i /home/website/public_html/media/videos/iphone/2407.mp4 -ss 779 -t 3 -i /home/website/public_html/media/videos/iphone/2407.mp4 -ss 876 -t 3 -i /home/website/public_html/media/videos/iphone/2407.mp4 -filter_complex "[0][1][2][3][4][5][6][7]concat=n=8:v=1:a=0",scale=400:240 -codec:v libx264 -unsharp -b:v 250k -maxrate 250k -bufsize 600k -qmin 10 -qmax 42 -threads 4 -an -y /home/website/public_html/media/videos/tmb/2407/video_copy.mp4

  PHP Info :
  PHP 5.6
  memory_limit 2001M
  max_execution_time 7200
  upload_max_filesize 2000M
  post_max_size 2000M
  max_input_time 7200
  exec is not disabled

  Server info
  CentOS Linux 7 (Core)
  ADVANCE-4 - AMD Epyc 7351P - 128GB DDR4 ECC 2400MHz - 2x HDD SATA 4TB Datacenter Class + 2x SSD NVMe 500GB Enterprise Class Soft RAID

  All help is really appreciated ! Thanks in advance

• how to make cv2.videoCapture.read() faster ?

  9 novembre 2022, par Yu-Long Tsai

  My question :

  



  I was working on my computer vision project. I use opencv(4.1.2) and python to implement it.

  



  I need a faster way to pass the reading frame into image processing on my Computer(Ubuntu 18.04 8 cores i7 3.00GHz Memory 32GB). the cv2.VideoCapture.read() read frame (frame size : 720x1280) will take about 120 140ms. which is too slow. my processing module take about 40ms per run. And we desire 25 30 FPS.

  



  here is my demo code so far :

  



  import cv2
from collections import deque
from time import sleep, time
import threading


class camCapture:
    def __init__(self, camID, buffer_size):
        self.Frame = deque(maxlen=buffer_size)
        self.status = False
        self.isstop = False
        self.capture = cv2.VideoCapture(camID)


    def start(self):
        print('camera started!')
        t1 = threading.Thread(target=self.queryframe, daemon=True, args=())
        t1.start()

    def stop(self):
        self.isstop = True
        print('camera stopped!')

    def getframe(self):
        print('current buffers : ', len(self.Frame))
        return self.Frame.popleft()

    def queryframe(self):
        while (not self.isstop):
            start = time()
            self.status, tmp = self.capture.read()
            print('read frame processed : ', (time() - start) *1000, 'ms')
            self.Frame.append(tmp)

        self.capture.release()

cam = camCapture(camID=0, buffer_size=50)
W, H = 1280, 720
cam.capture.set(cv2.CAP_PROP_FRAME_WIDTH, W)
cam.capture.set(cv2.CAP_PROP_FRAME_HEIGHT, H)


# start the reading frame thread
cam.start()

# filling frames
sleep(5)

while True:
  frame = cam.getframe() # numpy array shape (720, 1280, 3)

  cv2.imshow('video',frame)
  sleep( 40 / 1000) # mimic the processing time

  if cv2.waitKey(1) == 27:
        cv2.destroyAllWindows()
        cam.stop()
        break



  



  What I tried :

  



  


  1. multiThread - one thread just reading the frame, the other do the image processing things.
It's NOT what I want. because I could set a buffer deque saving 50 frames for example. but the frame-reading thread worked with the speed frame/130ms. my image processing thread worked with the speed frame/40ms. then the deque just running out. so I've been tried the solution. but not what I need.

  


  2. this topic is the discussion I found out which is most closest to my question. but unfortunately, I tried the accepted solutions (both of two below the discussion).

  


  



  One of the solution (6 six thumbs up) point out that he could reading and saving 100 frames at 1 sec intervals on his mac. why my machine cannot handle the frame reading work ? Do I missing something ? my installation used conda and pip conda install -c conda-forge opencv, pip install opencv-python(yes, I tried both.)

  



  The other of the solution(1 thumb up) using ffmpeg solution. but it seem's work with video file but not camera device ?

  



  

  3. adjust c2.waitKey() : 
the parameter just controls the frequency when video display. not a solution.

  


  



  Then, I know I just need some keywords to follow.

  



  code above is my demo code so far, I want some method or guide to make me videoCapture.read() faster. maybe a way to use multithread inside videoCapture object or other camera reading module.

  



  Any suggestions ?

  


• Why opencv videowriter is so slow ?

  22 février 2021, par user2267367

  Hi stackoverflow community,
I have a tricky problem and I need your help to understand what is going on here.
My program captures frames from a video grabber card (Blackmagic) which just works fine so far, at the same time I display the captured images with opencv (cv::imshow) which works good as well (But pretty cpu wasting).
The captured images are supposed to be stored on the disk as well, for this I put the captured Frames (cv::Mat) on a stack, to finally write them async with opencv :

  


  cv::VideoWriter videoWriter(path, cv::CAP_FFMPEG, fourcc, fps, *size);
videoWriter.set(cv::VIDEOWRITER_PROP_QUALITY, 100);

int id = metaDataWriter.insertNow(path);

while (this->isRunning) {

    while (!this->stackFrames.empty()) {

        cv:Mat m = this->stackFrames.pop();

        videoWriter << m;
    }
    
}

videoWriter.release();


  


  This code is running in an additional thread and will be stopped from outside.
The code is working so far, but it is sometimes pretty slow, which means my stack size increases and my system runs out of ram and get killed by the OS.

  


  Currently it is running on my developing system :

  


  

  • Ubuntu 18.04.05

  


  • OpenCV 4.4.0 compiled with Cuda

  


  • Intel i7 10. generation 32GB RAM, GPU Nvidia p620, M.2 SSD

  


  


  Depending on the codec (fourcc) this produces a high CPU load. So far I used mainly "MJPG", "x264". Sometimes even MJPG turns one core of the CPU to 100% load, and my stack raises until the programs run out of run. After a restart, sometimes, this problem is fixed, and it seems the load is distributed over all cores.

  


  Regarding to the Intel Doc. for my CPU, it has integrated hardware encoding/decoding for several codecs. But I guess opencv is not using them. Opencv even uses its own ffmpeg and not the one of my system. Here is my build command of opencv :

  


  cmake -D CMAKE_BUILD_TYPE=RELEASE \
-D CMAKE_INSTALL_PREFIX=/usr/local \
-D WITH_TBB=ON \
-D WITH_CUDA=ON \
-D BUILD_opencv_cudacodec=OFF \
-D ENABLE_FAST_MATH=1 \
-D CUDA_FAST_MATH=1 \
-D WITH_CUBLAS=1 \
-D WITH_V4L=ON \
-D WITH_QT=OFF \
-D WITH_OPENGL=ON \
-D WITH_GSTREAMER=ON \
-D OPENCV_GENERATE_PKGCONFIG=ON \
-D OPENCV_ENABLE_NONFREE=ON \
-D WITH_FFMPEG=1 \
-D OPENCV_EXTRA_MODULES_PATH=../../opencv_contrib/modules \
-D WITH_CUDNN=ON \
-D OPENCV_DNN_CUDA=ON \
-D CUDA_ARCH_BIN=6.1 ..


  


  I just started development with linux and C++, before I was working with Java/Maven, so the use of cmake is still a work in progress, pls go easy on me.

  


  Basically my question is, how can I make the video encoding/writing faster, use the hardware acceleration at best ?
Or if you think there is something else fishy, pls let me know.

  


  BR Michael