ffmpeg 옵션 순서가 은근히 까다로운가 보네 -_-

아래처럼 입력하면 이미지가 깨져서 변환된다.

$ ffmpeg -vcodec rawvideo -s 480x800 -f rawvideo -i fb1.cap_date -pix_fmt rgb565 -vf "transpose=2" output_1.png

 

이렇게 입력 코덱, 입력 비디오 포맷, 포맷에 따른 비디오 크기, 입력 파일 명 순서로 받고

출력시 회전, 출력 파일 명으로 넣어주어야 정상적으로 되는 듯.

$ ffmpeg -vcodec rawvideo -f rawvideo -pix_fmt rgb565 -s 480x800 -i fb1.cap_date -vf "transpose=2" output_1.png

 

---

// List available formats for ffmpeg
ffmpeg -pix_fmts

// Convert raw rgb565 image to png
ffmpeg -vcodec rawvideo -f rawvideo -pix_fmt rgb565 -s 1024x768 -i freescale_1024x768.raw -f image2 -vcodec png screen.png

[링크 : https://github-wiki-see.page/m/rogeriorps/ipu-examples/wiki/Converting-image-format-on-PC]

 

2번은 counter니까 반시계 90도(-=270도 회전)

ffmpeg -i in.mov -vf "transpose=1" out.mov
For the transpose parameter you can pass:

0 = 90CounterCLockwise and Vertical Flip (default)
1 = 90Clockwise
2 = 90CounterClockwise
3 = 90Clockwise and Vertical Flip

Use -vf "transpose=2,transpose=2" for 180 degrees.

[링크 : https://stackoverflow.com/questions/3937387/rotating-videos-with-ffmpeg

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Posted by 구차니
Linux2021. 10. 18. 14:38

일단은 아래와 같이 입력하면 에러가 나면서 안되고

$ convert -rotate 270 -size 480x800 fb1.cap_date converted.png
convert-im6.q16: no decode delegate for this image format `CAP_DATE' @ error/constitute.c/ReadImage/504.
convert-im6.q16: no images defined `converted.png' @ error/convert.c/ConvertImageCommand/3258.

 

원본 파일에 rgb: 라고 넣어주면 에러는 나지만 파일이 생성된다.

rotate 된 fb라(90도) 마저 더 돌려주면(+270도) 원래대로 나오는데, rgb565 fb 이미지라 정상적으로 변환되진 않는다.

$ convert -rotate 270 -size 480x800 rgb:fb1.cap_date converted.png
convert-im6.q16: unexpected end-of-file `fb1.cap_date': 그런 파일이나 디렉터리가 없습니다 @ error/rgb.c/ReadRGBImage/239.

[링크 : https://legacy.imagemagick.org/discourse-server/viewtopic.php?t=36078]

[링크 : https://legacy.imagemagick.org/discourse-server/viewtopic.php?t=33691]

[링크 : https://legacy.imagemagick.org/discourse-server/viewtopic.php?t=21341]

 

+

ffmpeg으로 하는 것 발견

[링크 : https://github-wiki-see.page/m/rogeriorps/ipu-examples/wiki/Converting-image-format-on-PC]

 

+

구버전이라 그런가 그게 아니면.. 빌드 하지 않으면 rgb565 옵션을 못쓰는걸까?

[링크 : https://imagemagick.org/script/formats.php]

 

+

[링크 : https://magick-bugs.imagemagick.narkive.com/4McQgU1e/converting-from-16-bit-rgb565-to-jpg-bmp]

 

+

$ convert -version
Version: ImageMagick 6.9.7-4 Q16 x86_64 20170114 http://www.imagemagick.org
Copyright: © 1999-2017 ImageMagick Studio LLC
License: http://www.imagemagick.org/script/license.php
Features: Cipher DPC Modules OpenMP
Delegates (built-in): bzlib djvu fftw fontconfig freetype jbig jng jpeg lcms lqr ltdl lzma openexr pangocairo png tiff wmf x xml zlib

 

$ convert -list format
   Format  Module    Mode  Description
-------------------------------------------------------------------------------
      3FR  DNG       r--   Hasselblad CFV/H3D39II
      AAI* AAI       rw+   AAI Dune image
       AI  PDF       rw-   Adobe Illustrator CS2
      ART* ART       rw-   PFS: 1st Publisher Clip Art
      ARW  DNG       r--   Sony Alpha Raw Image Format
      AVI  MPEG      r--   Microsoft Audio/Visual Interleaved
      AVS* AVS       rw+   AVS X image
      BGR* BGR       rw+   Raw blue, green, and red samples
     BGRA* BGR       rw+   Raw blue, green, red, and alpha samples
     BGRO* BGR       rw+   Raw blue, green, red, and opacity samples
      BIE* JBIG      rw-   Joint Bi-level Image experts Group interchange format (2.1)
      BMP* BMP       rw-   Microsoft Windows bitmap image
     BMP2* BMP       -w-   Microsoft Windows bitmap image (V2)
     BMP3* BMP       -w-   Microsoft Windows bitmap image (V3)
      BRF* BRAILLE   -w-   BRF ASCII Braille format
      CAL* CALS      rw-   Continuous Acquisition and Life-cycle Support Type 1
           Specified in MIL-R-28002 and MIL-PRF-28002
     CALS* CALS      rw-   Continuous Acquisition and Life-cycle Support Type 1
           Specified in MIL-R-28002 and MIL-PRF-28002
   CANVAS* XC        r--   Constant image uniform color
  CAPTION* CAPTION   r--   Caption
      CIN* CIN       rw-   Cineon Image File
      CIP* CIP       -w-   Cisco IP phone image format
     CLIP* CLIP      rw+   Image Clip Mask
     CMYK* CMYK      rw+   Raw cyan, magenta, yellow, and black samples
    CMYKA* CMYK      rw+   Raw cyan, magenta, yellow, black, and alpha samples
      CR2  DNG       r--   Canon Digital Camera Raw Image Format
      CRW  DNG       r--   Canon Digital Camera Raw Image Format
      CUR* ICON      rw-   Microsoft icon
      CUT* CUT       r--   DR Halo
     DATA* INLINE    rw+   Base64-encoded inline images
      DCM* DCM       r--   Digital Imaging and Communications in Medicine image
           DICOM is used by the medical community for images like X-rays.  The
           specification, "Digital Imaging and Communications in Medicine
           (DICOM)", is available at http://medical.nema.org/.  In particular,
           see part 5 which describes the image encoding (RLE, JPEG, JPEG-LS),
           and supplement 61 which adds JPEG-2000 encoding.
      DCR  DNG       r--   Kodak Digital Camera Raw Image File
      DCX* PCX       rw+   ZSoft IBM PC multi-page Paintbrush
      DDS* DDS       rw+   Microsoft DirectDraw Surface
    DFONT* TTF       r--   Multi-face font package (Freetype 2.8.1)
     DJVU* DJVU      r--   Deja vu
           See http://www.djvuzone.org/ for details about the DJVU format.  The
           DJVU 1.2 specification is available there and at
           ftp://swrinde.nde.swri.edu/pub/djvu/documents/.
      DNG  DNG       r--   Digital Negative
      DOT  DOT       ---   Graphviz
      DPX* DPX       rw-   SMPTE 268M-2003 (DPX 2.0)
           Digital Moving Picture Exchange Bitmap, Version 2.0.
           See SMPTE 268M-2003 specification at http://www.smtpe.org

     DXT1* DDS       rw+   Microsoft DirectDraw Surface
     DXT5* DDS       rw+   Microsoft DirectDraw Surface
     EPDF  PDF       rw-   Encapsulated Portable Document Format
      EPI  PS        rw-   Encapsulated PostScript Interchange format
      EPS  PS        rw-   Encapsulated PostScript
     EPS2* PS2       -w-   Level II Encapsulated PostScript
     EPS3* PS3       -w+   Level III Encapsulated PostScript
     EPSF  PS        rw-   Encapsulated PostScript
     EPSI  PS        rw-   Encapsulated PostScript Interchange format
      EPT  EPT       rw-   Encapsulated PostScript with TIFF preview
     EPT2  EPT       rw-   Encapsulated PostScript Level II with TIFF preview
     EPT3  EPT       rw+   Encapsulated PostScript Level III with TIFF preview
      ERF  DNG       r--   Epson Raw Format
      EXR  EXR       rw-   High Dynamic-range (HDR)
      FAX* FAX       rw+   Group 3 FAX
           FAX machines use non-square pixels which are 1.5 times wider than
           they are tall but computer displays use square pixels, therefore
           FAX images may appear to be narrow unless they are explicitly
           resized using a geometry of "150x100%".

     FILE* URL       r--   Uniform Resource Locator (file://)
     FITS* FITS      rw-   Flexible Image Transport System
  FRACTAL* PLASMA    r--   Plasma fractal image
      FTP* URL       r--   Uniform Resource Locator (ftp://)
      FTS* FITS      rw-   Flexible Image Transport System
       G3* FAX       rw-   Group 3 FAX
       G4* FAX       rw-   Group 4 FAX
      GIF* GIF       rw+   CompuServe graphics interchange format
    GIF87* GIF       rw-   CompuServe graphics interchange format (version 87a)
 GRADIENT* GRADIENT  r--   Gradual linear passing from one shade to another
     GRAY* GRAY      rw+   Raw gray samples
   GROUP4* TIFF      rw-   Raw CCITT Group4
       GV  DOT       ---   Graphviz
        H* MAGICK    -w-   Image expressed as a 'C/C++' char array
     HALD* HALD      r--   Identity Hald color lookup table image
      HDR* HDR       rw+   Radiance RGBE image format
HISTOGRAM* HISTOGRAM -w-   Histogram of the image
      HRZ* HRZ       rw-   Slow Scan TeleVision
      HTM* HTML      -w-   Hypertext Markup Language and a client-side image map
     HTML* HTML      -w-   Hypertext Markup Language and a client-side image map
     HTTP* URL       r--   Uniform Resource Locator (http://)
    HTTPS* URL       ---   Uniform Resource Locator (https://)
      ICB* TGA       rw-   Truevision Targa image
      ICO* ICON      rw+   Microsoft icon
     ICON* ICON      rw-   Microsoft icon
      IIQ  DNG       r--   Phase One Raw Image Format
     INFO  INFO      -w+   The image format and characteristics
   INLINE* INLINE    rw+   Base64-encoded inline images
      IPL* IPL       rw+   IPL Image Sequence
   ISOBRL* BRAILLE   -w-   ISO/TR 11548-1 format
  ISOBRL6* BRAILLE   -w-   ISO/TR 11548-1 format 6dot
      JBG* JBIG      rw+   Joint Bi-level Image experts Group interchange format (2.1)
     JBIG* JBIG      rw+   Joint Bi-level Image experts Group interchange format (2.1)
      JNG* PNG       rw-   JPEG Network Graphics
           See http://www.libpng.org/pub/mng/ for details about the JNG
           format.
      JNX* JNX       r--   Garmin tile format
      JPE* JPEG      rw-   Joint Photographic Experts Group JFIF format (80)
     JPEG* JPEG      rw-   Joint Photographic Experts Group JFIF format (80)
      JPG* JPEG      rw-   Joint Photographic Experts Group JFIF format (80)
      JPS* JPEG      rw-   Joint Photographic Experts Group JFIF format (80)
     JSON  JSON      -w+   The image format and characteristics
      K25  DNG       r--   Kodak Digital Camera Raw Image Format
      KDC  DNG       r--   Kodak Digital Camera Raw Image Format
    LABEL* LABEL     r--   Image label
      M2V  MPEG      rw+   MPEG Video Stream
      M4V  MPEG      rw+   Raw MPEG-4 Video
      MAC* MAC       r--   MAC Paint
   MAGICK* MAGICK    rw-   Predefined Magick Image (LOGO, ROSE, etc.); output same as 'H'
      MAP* MAP       rw-   Colormap intensities and indices
     MASK* MASK      rw+   Image Clip Mask
      MAT  MAT       rw+   MATLAB level 5 image format
    MATTE* MATTE     -w+   MATTE format
      MEF  DNG       r--   Mamiya Raw Image File
     MIFF* MIFF      rw+   Magick Image File Format
      MKV  MPEG      rw+   Multimedia Container
      MNG* PNG       rw+   Multiple-image Network Graphics (libpng 1.6.34)
           See http://www.libpng.org/pub/mng/ for details about the MNG
           format.
     MONO* MONO      rw-   Raw bi-level bitmap
      MOV  MPEG      rw+   MPEG Video Stream
      MP4  MPEG      rw+   MPEG-4 Video Stream
      MPC* MPC       rw+   Magick Persistent Cache image format
     MPEG  MPEG      rw+   MPEG Video Stream
      MPG  MPEG      rw+   MPEG Video Stream
      MRW  DNG       r--   Sony (Minolta) Raw Image File
      MSL* MSL       rw+   Magick Scripting Language
     MSVG  SVG       rw+   ImageMagick's own SVG internal renderer
      MTV* MTV       rw+   MTV Raytracing image format
      MVG* MVG       rw-   Magick Vector Graphics
      NEF  DNG       r--   Nikon Digital SLR Camera Raw Image File
      NRW  DNG       r--   Nikon Digital SLR Camera Raw Image File
     NULL* NULL      rw-   Constant image of uniform color
      ORF  DNG       r--   Olympus Digital Camera Raw Image File
      OTB* OTB       rw-   On-the-air bitmap
      OTF* TTF       r--   Open Type font (Freetype 2.8.1)
      PAL* UYVY      rw-   16bit/pixel interleaved YUV
     PALM* PALM      rw+   Palm pixmap
      PAM* PNM       rw+   Common 2-dimensional bitmap format
    PANGO* PANGO     r--   Pango Markup Language (Pangocairo 1.40.14)
  PATTERN* PATTERN   r--   Predefined pattern
      PBM* PNM       rw+   Portable bitmap format (black and white)
      PCD* PCD       rw-   Photo CD
     PCDS* PCD       rw-   Photo CD
      PCL  PCL       rw+   Printer Control Language
      PCT* PICT      rw-   Apple Macintosh QuickDraw/PICT
      PCX* PCX       rw-   ZSoft IBM PC Paintbrush
      PDB* PDB       rw+   Palm Database ImageViewer Format
      PDF  PDF       rw+   Portable Document Format
     PDFA  PDF       rw+   Portable Document Archive Format
      PEF  DNG       r--   Pentax Electronic File
      PES* PES       r--   Embrid Embroidery Format
      PFA* TTF       r--   Postscript Type 1 font (ASCII) (Freetype 2.8.1)
      PFB* TTF       r--   Postscript Type 1 font (binary) (Freetype 2.8.1)
      PFM* PFM       rw+   Portable float format
      PGM* PNM       rw+   Portable graymap format (gray scale)
    PICON* XPM       rw-   Personal Icon
     PICT* PICT      rw-   Apple Macintosh QuickDraw/PICT
      PIX* PIX       r--   Alias/Wavefront RLE image format
    PJPEG* JPEG      rw-   Joint Photographic Experts Group JFIF format (80)
   PLASMA* PLASMA    r--   Plasma fractal image
      PNG* PNG       rw-   Portable Network Graphics (libpng 1.6.34)
           See http://www.libpng.org/ for details about the PNG format.
    PNG00* PNG       rw-   PNG inheriting bit-depth, color-type from original if possible
    PNG24* PNG       rw-   opaque or binary transparent 24-bit RGB (zlib 1.2.11)
    PNG32* PNG       rw-   opaque or transparent 32-bit RGBA
    PNG48* PNG       rw-   opaque or binary transparent 48-bit RGB
    PNG64* PNG       rw-   opaque or transparent 64-bit RGBA
     PNG8* PNG       rw-   8-bit indexed with optional binary transparency
      PNM* PNM       rw+   Portable anymap
      PPM* PNM       rw+   Portable pixmap format (color)
  PREVIEW* PREVIEW   -w-   Show a preview an image enhancement, effect, or f/x
       PS  PS        rw+   PostScript
      PS2* PS2       -w+   Level II PostScript
      PS3* PS3       -w+   Level III PostScript
      PSB* PSD       rw+   Adobe Large Document Format
      PSD* PSD       rw+   Adobe Photoshop bitmap
     PTIF* TIFF      rw+   Pyramid encoded TIFF
      PWP* PWP       r--   Seattle Film Works
RADIAL-GRADIENT* GRADIENT  r--   Gradual radial passing from one shade to another
      RAF  DNG       r--   Fuji CCD-RAW Graphic File
      RAS* SUN       rw+   SUN Rasterfile
      RAW  DNG       r--   Raw
      RGB* RGB       rw+   Raw red, green, and blue samples
     RGBA* RGB       rw+   Raw red, green, blue, and alpha samples
     RGBO* RGB       rw+   Raw red, green, blue, and opacity samples
      RGF* RGF       rw-   LEGO Mindstorms EV3 Robot Graphic Format (black and white)
      RLA* RLA       r--   Alias/Wavefront image
      RLE* RLE       r--   Utah Run length encoded image
      RMF  DNG       r--   Raw Media Format
      RW2  DNG       r--   Panasonic Lumix Raw Image
      SCR* SCR       r--   ZX-Spectrum SCREEN$
      SCT* SCT       r--   Scitex HandShake
      SFW* SFW       r--   Seattle Film Works
      SGI* SGI       rw+   Irix RGB image
    SHTML* HTML      -w-   Hypertext Markup Language and a client-side image map
      SIX* SIX       rw-   DEC SIXEL Graphics Format
    SIXEL* SIXEL     rw-   DEC SIXEL Graphics Format
SPARSE-COLOR* TXT       -w+   Sparse Color
      SR2  DNG       r--   Sony Raw Format 2
      SRF  DNG       r--   Sony Raw Format
  STEGANO* STEGANO   r--   Steganographic image
      SUN* SUN       rw+   SUN Rasterfile
      SVG  SVG       rw+   Scalable Vector Graphics (XML 2.9.4)
     SVGZ  SVG       rw+   Compressed Scalable Vector Graphics (XML 2.9.4)
     TEXT* TXT       rw+   Text
      TGA* TGA       rw-   Truevision Targa image
THUMBNAIL* THUMBNAIL -w+   EXIF Profile Thumbnail
     TIFF* TIFF      rw+   Tagged Image File Format (LIBTIFF, Version 4.0.9)
   TIFF64* TIFF      rw-   Tagged Image File Format (64-bit) (LIBTIFF, Version 4.0.9)
     TILE* TILE      r--   Tile image with a texture
      TIM* TIM       r--   PSX TIM
      TTC* TTF       r--   TrueType font collection (Freetype 2.8.1)
      TTF* TTF       r--   TrueType font (Freetype 2.8.1)
      TXT* TXT       rw+   Text
     UBRL* BRAILLE   -w-   Unicode Text format
    UBRL6* BRAILLE   -w-   Unicode Text format 6dot
      UIL* UIL       -w-   X-Motif UIL table
     UYVY* UYVY      rw-   16bit/pixel interleaved YUV
      VDA* TGA       rw-   Truevision Targa image
    VICAR* VICAR     rw-   VICAR rasterfile format
      VID* VID       rw+   Visual Image Directory
     VIFF* VIFF      rw+   Khoros Visualization image
     VIPS* VIPS      rw+   VIPS image
      VST* TGA       rw-   Truevision Targa image
     WBMP* WBMP      rw-   Wireless Bitmap (level 0) image
      WMF* WMF       r--   Windows Meta File
      WMV  MPEG      rw+   Windows Media Video
      WMZ* WMF       r--   Compressed Windows Meta File
      WPG* WPG       r--   Word Perfect Graphics
        X* X         rw+   X Image
      X3F  DNG       r--   Sigma Camera RAW Picture File
      XBM* XBM       rw-   X Windows system bitmap (black and white)
       XC* XC        r--   Constant image uniform color
      XCF* XCF       r--   GIMP image
      XPM* XPM       rw-   X Windows system pixmap (color)
      XPS  XPS       r--   Microsoft XML Paper Specification
       XV* VIFF      rw+   Khoros Visualization image
      XWD* XWD       rw-   X Windows system window dump (color)
    YCbCr* YCbCr     rw+   Raw Y, Cb, and Cr samples
   YCbCrA* YCbCr     rw+   Raw Y, Cb, Cr, and alpha samples
      YUV* YUV       rw-   CCIR 601 4:1:1 or 4:2:2

* native blob support
r read support
w write support
+ support for multiple images

 

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목표가 없어서 인지

아니면 할일이 딱히 정해진게 없어서 인진 모르겠지만

축~ 늘어진다.

 

아니면 얼마전 맞은 백신 2차 때문은 아니겠지?

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애들 둘 데리고 자서 그런가.. 피로가 안풀려서

낮잠으로 오전을 그냥 날려버림 -_-

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개소리 왈왈/컴퓨터2021. 10. 15. 14:29

오늘따라 유난히 빌드하다 죽는 것 같아서 syslog를 보고 있노라니 이런 메시지가 뿜뿜한다.

Oct 15 13:50:43 flex kernel: [ 7509.905180] mce: CPU0: Core temperature above threshold, cpu clock throttled (total events = 23935)
Oct 15 13:50:43 flex kernel: [ 7509.905182] mce: CPU3: Package temperature above threshold, cpu clock throttled (total events = 26108)
Oct 15 13:50:43 flex kernel: [ 7509.905183] mce: CPU2: Package temperature above threshold, cpu clock throttled (total events = 26108)
Oct 15 13:50:43 flex kernel: [ 7509.905183] mce: CPU4: Core temperature above threshold, cpu clock throttled (total events = 23935)
Oct 15 13:50:43 flex kernel: [ 7509.905184] mce: CPU6: Package temperature above threshold, cpu clock throttled (total events = 26108)
Oct 15 13:50:43 flex kernel: [ 7509.905185] mce: CPU7: Package temperature above threshold, cpu clock throttled (total events = 26108)
Oct 15 13:50:43 flex kernel: [ 7509.905185] mce: CPU4: Package temperature above threshold, cpu clock throttled (total events = 26108)
Oct 15 13:50:43 flex kernel: [ 7509.905186] mce: CPU0: Package temperature above threshold, cpu clock throttled (total events = 26108)
Oct 15 13:50:43 flex kernel: [ 7509.905212] mce: CPU1: Package temperature above threshold, cpu clock throttled (total events = 26108)
Oct 15 13:50:43 flex kernel: [ 7509.905213] mce: CPU5: Package temperature above threshold, cpu clock throttled (total events = 26108)
Oct 15 13:50:43 flex kernel: [ 7509.906081] mce: CPU4: Core temperature/speed normal
Oct 15 13:50:43 flex kernel: [ 7509.906081] mce: CPU0: Core temperature/speed normal
Oct 15 13:50:43 flex kernel: [ 7509.906082] mce: CPU6: Package temperature/speed normal
Oct 15 13:50:43 flex kernel: [ 7509.906083] mce: CPU3: Package temperature/speed normal
Oct 15 13:50:43 flex kernel: [ 7509.906083] mce: CPU2: Package temperature/speed normal
Oct 15 13:50:43 flex kernel: [ 7509.906084] mce: CPU7: Package temperature/speed normal
Oct 15 13:50:43 flex kernel: [ 7509.906084] mce: CPU0: Package temperature/speed normal
Oct 15 13:50:43 flex kernel: [ 7509.906085] mce: CPU4: Package temperature/speed normal
Oct 15 13:50:43 flex kernel: [ 7509.906114] mce: CPU5: Package temperature/speed normal
Oct 15 13:50:43 flex kernel: [ 7509.906115] mce: CPU1: Package temperature/speed normal

 

몇 도 까지 올라가나 보니 측정 한계 온도가 설마 90은 아니겠지?

$ sensors
coretemp-isa-0000
Adapter: ISA adapter
Package id 0:  +90.0°C  (high = +100.0°C, crit = +100.0°C)
Core 0:        +90.0°C  (high = +100.0°C, crit = +100.0°C)
Core 1:        +87.0°C  (high = +100.0°C, crit = +100.0°C)
Core 2:        +87.0°C  (high = +100.0°C, crit = +100.0°C)
Core 3:        +86.0°C  (high = +100.0°C, crit = +100.0°C)

 

+

참고로 idle

$ sensors
coretemp-isa-0000
Adapter: ISA adapter
Package id 0:  +43.0°C  (high = +100.0°C, crit = +100.0°C)
Core 0:        +42.0°C  (high = +100.0°C, crit = +100.0°C)
Core 1:        +42.0°C  (high = +100.0°C, crit = +100.0°C)
Core 2:        +41.0°C  (high = +100.0°C, crit = +100.0°C)
Core 3:        +42.0°C  (high = +100.0°C, crit = +100.0°C)

 

심심해서(?) 검색해보니 삼성 플렉스 i7는 사지 말라고 하는데

플렉스나 플렉스 알파나 그게 그거니.. 이래서 그런 소리가 나온건가?

Intel(R) Core(TM) i7-10510U CPU @ 1.80GHz

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embeded/i.mx 8m plus2021. 10. 14. 15:57

어떤 라이브러리에서 하나 뒤져보는데 일단 tensorflow 소스에는 없고

file system에서 뒤져보는데 /usr/lib/libovxlib.so.1.1.0 파일에서 발견된다.

후.. 추적은 일단 포기

 

lrwxrwxrwx 1 root root      18 Mar  9  2018 /usr/lib/libovxlib.so.1 -> libovxlib.so.1.1.0
lrwxrwxrwx 1 root root      18 Mar  9  2018 /usr/lib/libovxlib.so.1.1 -> libovxlib.so.1.1.0
-rwxr-xr-x 1 root root 3705768 Mar  9  2018 /usr/lib/libovxlib.so.1.1.0

 

INFO: Loaded model my_model.tflite
INFO: resolved reporter
INFO: Created TensorFlow Lite delegate for NNAPI.
INFO: Use NNAPI acceleration.
WARNING: Operator RESIZE_BILINEAR (v3) refused by NNAPI delegate: Operator refused due performance reasons.
INFO: Applied NNAPI delegate.
W [vsi_nn_op_eltwise_setup:178]Output size mismatch, expect 917504, but got 50176
E [setup_node:448]Setup node[52] PRELU fail
W [vsi_nn_op_eltwise_setup:178]Output size mismatch, expect 917504, but got 50176
E [setup_node:448]Setup node[52] PRELU fail
ERROR: NN API returned error ANEURALNETWORKS_BAD_DATA at line 4151 while running computation.

ERROR: Node number 56 (TfLiteNnapiDelegate) failed to invoke.

ERROR: Failed to invoke tflite!

 

+

coco ssd mobilenet v1 - object detection은 정상적으로 작동한다

# time ./label_image -m 1.tflite -a 1
INFO: Loaded model 1.tflite
INFO: resolved reporter
INFO: Created TensorFlow Lite delegate for NNAPI.
INFO: Use NNAPI acceleration.
WARNING: Operator CUSTOM (v1) refused by NNAPI delegate: Unsupported operation type.
INFO: Applied NNAPI delegate.
INFO: invoked
INFO: average time: 13.178 ms
INFO: 0.00389769: 3 great white shark
INFO: 0.0038741: 2 goldfish

real    0m5.722s
user    0m5.573s
sys     0m0.136s

[링크 : https://www.tensorflow.org/lite/examples/object_detection/overview]

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처음 패키지가 본체(?)고 그 위에는 한글언어 인식 데이터 패키지

$ sudo apt install tesseract-ocr tesseract-ocr-kor tesseract-ocr-script-hang tesseract-ocr-script-hang-vert

 

도움말을 보는데 도움은 안된다(응?)

리눅스에서 실행시 outputbase를 stdout으로 하면 콘솔에 텍스트로 출력된다.

$ tesseract --help
Usage:
  tesseract --help | --help-extra | --version
  tesseract --list-langs
  tesseract imagename outputbase [options...] [configfile...]

OCR options:
  -l LANG[+LANG]        Specify language(s) used for OCR.
NOTE: These options must occur before any configfile.

Single options:
  --help                Show this help message.
  --help-extra          Show extra help for advanced users.
  --version             Show version information.
  --list-langs          List available languages for tesseract engine.

$ tesseract --list-langs
List of available languages (5):
Hangul
Hangul_vert
eng
kor
osd

 

LSTM 학습

[링크 : https://hongjong.tistory.com/19]

[링크 : https://diyworld.tistory.com/114]

[링크 : https://davelogs.tistory.com/70]

[링크 : https://davelogs.tistory.com/72]

[링크 : https://tesseract-ocr.github.io/tessdoc/]

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embeded/i.mx 8m plus2021. 10. 13. 14:38

LF_v5.10.52-2.1.0_images_IMX8MPEVK.zip 파일을 받아서 이미지를 sd 카드에 굽고

부팅해서 들어가보니 경로가 좀 다르다.

tensorflow 2.5.0 버전이면.. 쓸 수 있는 건가?

# cd /usr/bin/tensorflow-lite-2.5.0/examples
# ./benchmark_model --graph=mobilenet_v1_1.0_224_quant.tflite
STARTING!
Log parameter values verbosely: [0]
Graph: [mobilenet_v1_1.0_224_quant.tflite]
Use VXdelegate : [0]
Loaded model mobilenet_v1_1.0_224_quant.tflite
The input model file size (MB): 4.27635
Initialized session in 1.807ms.
Running benchmark for at least 1 iterations and at least 0.5 seconds but terminate if exceeding 150 seconds.
count=4 first=167959 curr=162606 min=162606 max=167959 avg=164253 std=2159

Running benchmark for at least 50 iterations and at least 1 seconds but terminate if exceeding 150 seconds.
count=50 first=162727 curr=163003 min=162308 max=163308 avg=162758 std=190

Inference timings in us: Init: 1807, First inference: 167959, Warmup (avg): 164253, Inference (avg): 162758
Note: as the benchmark tool itself affects memory footprint, the following is only APPROXIMATE to the actual memory footprint of the model at runtime. Take the information at your discretion.
Peak memory footprint (MB): init=2.51562 overall=8.64062

# ./benchmark_model --graph=mobilenet_v1_1.0_224_quant.tflite --use_nnapi=true
STARTING!
Log parameter values verbosely: [0]
Graph: [mobilenet_v1_1.0_224_quant.tflite]
Use NNAPI: [1]
NNAPI accelerators available: [vsi-npu]
Use VXdelegate : [0]
Loaded model mobilenet_v1_1.0_224_quant.tflite
INFO: Created TensorFlow Lite delegate for NNAPI.
Explicitly applied NNAPI delegate, and the model graph will be completely executed by the delegate.
The input model file size (MB): 4.27635
Initialized session in 4.183ms.
Running benchmark for at least 1 iterations and at least 0.5 seconds but terminate if exceeding 150 seconds.
count=1 curr=4649626

Running benchmark for at least 50 iterations and at least 1 seconds but terminate if exceeding 150 seconds.
count=360 first=2665 curr=2733 min=2632 max=2783 avg=2715.67 std=16

Inference timings in us: Init: 4183, First inference: 4649626, Warmup (avg): 4.64963e+06, Inference (avg): 2715.67
Note: as the benchmark tool itself affects memory footprint, the following is only APPROXIMATE to the actual memory footprint of the model at runtime. Take the information at your discretion.
Peak memory footprint (MB): init=2.59766 overall=30.1836

 

label_image로 해보면.. warm up이 먼진 모르겠지만 invoke() 함수 자체는 짧게 걸리는데

그 이전에 먼가 하는게 오래 걸리는지 cpu만으로 돌리는 것 보다 4초 이상 오래 걸린다.

# time ./label_image -w 1
INFO: Loaded model ./mobilenet_v1_1.0_224_quant.tflite
INFO: resolved reporter
INFO: invoked
INFO: average time: 43.865 ms
INFO: 0.764706: 653 military uniform
INFO: 0.121569: 907 Windsor tie
INFO: 0.0156863: 458 bow tie
INFO: 0.0117647: 466 bulletproof vest
INFO: 0.00784314: 835 suit

real    0m0.142s
user    0m0.385s
sys     0m0.020s

# time ./label_image -w 1 -a 1
INFO: Loaded model ./mobilenet_v1_1.0_224_quant.tflite
INFO: resolved reporter
INFO: Created TensorFlow Lite delegate for NNAPI.
INFO: Use NNAPI acceleration.
INFO: Applied NNAPI delegate.
INFO: invoked
INFO: average time: 2.797 ms
INFO: 0.768627: 653 military uniform
INFO: 0.105882: 907 Windsor tie
INFO: 0.0196078: 458 bow tie
INFO: 0.0117647: 466 bulletproof vest
INFO: 0.00784314: 835 suit

real    0m4.748s
user    0m4.648s
sys     0m0.092s

 

아래는 2.1.0 버전에 맞춰서 한 구버전 문서 내용 인 듯.

$ cd /usr/bin/tensorflow-lite-2.1.0/examples
$ ./benchmark_model --graph=mobilenet_v1_1.0_224_quant.tflite
$: ./benchmark_model --graph=mobilenet_v1_1.0_224_quant.tflite --use_nnapi=true

./lbl_img -i grace_hopper.bmp -l labels.txt -w 1
./lbl_img -i grace_hopper.bmp -l labels.txt -w 1 -a 1

[링크 : https://www.mouser.com/pdfDocs/AN12964.pdf]

 

 

+

망할 놈(?)들 도움말이랑 다르잖아?!

# ./label_image --help
ERROR: usage: ./label_image <flags>
Flags:
        --num_threads=1                 int32   optional        number of threads used for inference on CPU.
        --max_delegated_partitions=0    int32   optional        Max number of partitions to be delegated.
        --min_nodes_per_partition=0     int32   optional        The minimal number of TFLite graph nodes of a partition that has to be reached for it to be delegated.A negative value or 0 means to use the default choice of each delegate.
        --num_threads=1                 int32   optional        number of threads used for inference on CPU.
        --max_delegated_partitions=0    int32   optional        Max number of partitions to be delegated.
        --min_nodes_per_partition=0     int32   optional        The minimal number of TFLite graph nodes of a partition that has to be reached for it to be delegated.A negative value or 0 means to use the default choice of each delegate.
        --use_xnnpack=false             bool    optional        use XNNPack
        --use_nnapi=false               bool    optional        use nnapi delegate api
        --nnapi_execution_preference=   string  optional        execution preference for nnapi delegate. Should be one of the following: fast_single_answer, sustained_speed, low_power, undefined
        --nnapi_execution_priority=     string  optional        The model execution priority in nnapi, and it should be one of the following: default, low, medium and high. This requires Android 11+.
        --nnapi_accelerator_name=       string  optional        the name of the nnapi accelerator to use (requires Android Q+)
        --disable_nnapi_cpu=true        bool    optional        Disable the NNAPI CPU device
        --nnapi_allow_fp16=false        bool    optional        Allow fp32 computation to be run in fp16

 

    static struct option long_options[] = {
        {"accelerated", required_argument, nullptr, 'a'},
        {"allow_fp16", required_argument, nullptr, 'f'},
        {"count", required_argument, nullptr, 'c'},
        {"verbose", required_argument, nullptr, 'v'},
        {"image", required_argument, nullptr, 'i'},
        {"labels", required_argument, nullptr, 'l'},
        {"tflite_model", required_argument, nullptr, 'm'},
        {"profiling", required_argument, nullptr, 'p'},
        {"threads", required_argument, nullptr, 't'},
        {"input_mean", required_argument, nullptr, 'b'},
        {"input_std", required_argument, nullptr, 's'},
        {"num_results", required_argument, nullptr, 'r'},
        {"max_profiling_buffer_entries", required_argument, nullptr, 'e'},
        {"warmup_runs", required_argument, nullptr, 'w'},
        {"gl_backend", required_argument, nullptr, 'g'},
        {"hexagon_delegate", required_argument, nullptr, 'j'},
        {"xnnpack_delegate", required_argument, nullptr, 'x'},
        {nullptr, 0, nullptr, 0}};

[링크 : https://github.com/tensorflow/tensorflow/blob/v2.5.0/tensorflow/lite/examples/label_image/label_image.cc]

 

+

그러면.. 어떤식으로 라이브러리를 빌드해서 저게 가능해진거지?

# ldd label_image
        linux-vdso.so.1 (0x0000ffffa0989000)
        libtensorflow-lite.so.2.5.0 => /usr/lib/libtensorflow-lite.so.2.5.0 (0x0000ffffa05ab000)
        libm.so.6 => /lib/libm.so.6 (0x0000ffffa0501000)
        libstdc++.so.6 => /usr/lib/libstdc++.so.6 (0x0000ffffa032a000)
        libgcc_s.so.1 => /lib/libgcc_s.so.1 (0x0000ffffa0305000)
        libc.so.6 => /lib/libc.so.6 (0x0000ffffa0190000)
        /lib/ld-linux-aarch64.so.1 (0x0000ffffa0957000)
        libtim-vx.so => /usr/lib/libtim-vx.so (0x0000ffffa00c7000)
        libdl.so.2 => /lib/libdl.so.2 (0x0000ffffa00b1000)
        libpthread.so.0 => /lib/libpthread.so.0 (0x0000ffffa0082000)
        librt.so.1 => /lib/librt.so.1 (0x0000ffffa006a000)
        libovxlib.so.1.1.0 => /usr/lib/libovxlib.so.1.1.0 (0x0000ffff9fcd1000)
        libOpenVX.so.1 => /usr/lib/libOpenVX.so.1 (0x0000ffff9fa7e000)
        libVSC.so => /usr/lib/libVSC.so (0x0000ffff9eae2000)
        libGAL.so => /usr/lib/libGAL.so (0x0000ffff9e91b000)
        libArchModelSw.so => /usr/lib/libArchModelSw.so (0x0000ffff9e8f3000)
        libNNArchPerf.so => /usr/lib/libNNArchPerf.so (0x0000ffff9e8d0000)

 

 

+

PRELU 연산자 자체는 지원하는 것 같은데 output size mistach가 원인인가?

INFO: Use NNAPI acceleration.
WARNING: Operator RESIZE_BILINEAR (v3) refused by NNAPI delegate: Operator refused due performance reasons.
INFO: Applied NNAPI delegate.
W [vsi_nn_op_eltwise_setup:178]Output size mismatch, expect 917504, but got 50176
E [setup_node:448]Setup node[52] PRELU fail
W [vsi_nn_op_eltwise_setup:178]Output size mismatch, expect 917504, but got 50176
E [setup_node:448]Setup node[52] PRELU fail
ERROR: NN API returned error ANEURALNETWORKS_BAD_DATA at line 4151 while running computation.

ERROR: Node number 56 (TfLiteNnapiDelegate) failed to invoke.

ERROR: Failed to invoke tflite!

[링크 : https://www.nxp.com/docs/en/user-guide/IMX-MACHINE-LEARNING-UG.pdf]

 

 

+

warm up은 코드상으로 1회 invoke 하는 것인데 해당 작업이 4649ms 정도 소요되며

warm up 없이 1회 실행하면 대략 그 정도 시간이 소요된다.

root@imx8mpevk:/usr/bin/tensorflow-lite-2.5.0/examples# time ./label_image -a 1 -w 0 -p 1 -c 1
INFO: Loaded model ./mobilenet_v1_1.0_224_quant.tflite
INFO: resolved reporter
INFO: Created TensorFlow Lite delegate for NNAPI.
INFO: Use NNAPI acceleration.
INFO: Applied NNAPI delegate.
INFO: invoked
INFO: average time: 4649.78 ms
INFO: 0.768627: 653 military uniform
INFO: 0.105882: 907 Windsor tie
INFO: 0.0196078: 458 bow tie
INFO: 0.0117647: 466 bulletproof vest
INFO: 0.00784314: 835 suit

real    0m4.757s
user    0m4.655s
sys     0m0.096s
root@imx8mpevk:/usr/bin/tensorflow-lite-2.5.0/examples# time ./label_image -a 1 -w 0 -p 1 -c 4
INFO: Loaded model ./mobilenet_v1_1.0_224_quant.tflite
INFO: resolved reporter
INFO: Created TensorFlow Lite delegate for NNAPI.
INFO: Use NNAPI acceleration.
INFO: Applied NNAPI delegate.
INFO: invoked
INFO: average time: 1164.36 ms
INFO: 0.768627: 653 military uniform
INFO: 0.105882: 907 Windsor tie
INFO: 0.0196078: 458 bow tie
INFO: 0.0117647: 466 bulletproof vest
INFO: 0.00784314: 835 suit

real    0m4.768s
user    0m4.663s
sys     0m0.092s
root@imx8mpevk:/usr/bin/tensorflow-lite-2.5.0/examples# time ./label_image -a 1 -w 0 -p 1 -c 10000
INFO: Loaded model ./mobilenet_v1_1.0_224_quant.tflite
INFO: resolved reporter
INFO: Created TensorFlow Lite delegate for NNAPI.
INFO: Use NNAPI acceleration.
INFO: Applied NNAPI delegate.
INFO: invoked
INFO: average time: 3.30189 ms
INFO: 0.768627: 653 military uniform
INFO: 0.105882: 907 Windsor tie
INFO: 0.0196078: 458 bow tie
INFO: 0.0117647: 466 bulletproof vest
INFO: 0.00784314: 835 suit

real    0m33.128s
user    0m7.516s
sys     0m1.590s

 

openVX를 통해 처리하는 것 같은데 처음 처리하면 그래프 처리 결과를 스토리지에 저장한다고.

11.3 Hardware accelerators warmup time
For both Arm NN and TensorFlow Lite, the initial execution of model inference takes longer time, because of the model graph initialization needed by the GPU/NPU hardware accelerator. The initialization phase is known as warmup. This time duration can be decreased for subsequent application that runs by storing on disk the information resulted from the initial OpenVX graph processing. The following environment variables should be used for this purpose:
VIV_VX_ENABLE_CACHE_GRAPH_BINARY: flag to enable/disable OpenVX graph caching
VIV_VX_CACHE_BINARY_GRAPH_DIR: set location of the cached information on disk
For example, set these variables on the console in this way:
export VIV_VX_ENABLE_CACHE_GRAPH_BINARY="1"
export VIV_VX_CACHE_BINARY_GRAPH_DIR=`pwd`

[링크 : https://www.nxp.com/docs/en/user-guide/IMX-MACHINE-LEARNING-UG.pdf]

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embeded/i.mx 8m plus2021. 10. 13. 11:44

오잉? 저번에 볼 땐 8M PLUS에는 cortex-M 계열 없었던 것 같은데?!?!

[링크 : https://www.nxp.com/products/processors-and-microcontrollers/arm-processors/i-mx-applications-processors/i-mx-8-processors/i-mx-8m-plus-arm-cortex-a53-machine-learning-vision-multimedia-and-industrial-iot:IMX8MPLUS]

 

음.. 그냥 내 눈이 삐꾸인걸로 -_ㅠ

[링크 : https://www.nxp.com/products/processors-and-microcontrollers/arm-processors/i-mx-applications-processors/i-mx-8-processors:IMX8-SERIES]

 

아무튼 회사에 굴러(?) 다니는 이 녀석 사용해보려니

헐.. 무슨 디버그 포트가 이렇게 많이 인식 돼? 일단 나의 경우에는 리눅스가 COM27로 연결되었다.

[링크 : https://www.nxp.com/design/development-boards/i-mx-evaluation-and-development-boards/evaluation-kit-for-the-i-mx-8m-plus-applications-processor:8MPLUSLPD4-EVK]

 

패키지에 들어있던 종이 쪼가리구만 -_-

첫째랑 둘째는 누구꺼냐!?

Four UART connections will appear on the PC, the third port for the Cortex-A53 core and the fourth for Cortex-M7 core system debugging.

[링크 : https://www.nxp.com/docs/en/quick-reference-guide/8MPLUSEVKQSG.pdf]

 

Proejct - Tutorial에 Machine Learning

[링크 : https://www.nxp.com/document/guide/getting-started-with-the-i-mx-8m-plus-evk:GS-iMX-8M-Plus-EVK]

 

i.MX 8M PLUS 에는 전체 기능을 다 지원하는데

NPU를 써볼려면 eIQ를 이용해서 먼가 짓을 해야 하는 것 같고.

Cortex-M7도 있으니 (standalone 혹은 collaborative 하게 작동이 가능하다고) 이걸 이용해서 일종의 가속기화 하려나?

 

TFLite

[링크 : https://www.nxp.com/design/software/development-software/eiq-ml-development-environment/eiq-inference-with-tensorflow-lite:eIQTensorFlowLite]

 

TFLite for MCU

[링크 : https://www.nxp.com/design/software/development-software/eiq-ml-development-environment/eiq-inference-with-tensorflow-lite-micro:EIQ-TFLITE-MICRO]

 

위에서 다운로드 링크 누르니 이상한데(?)로 보내버리네

[링크 : https://mcuxpresso.nxp.com/en/welcome] cortex-M7 쓰려면 이게 필요한 듯. 이클립스 기반?

[링크 : https://source.codeaurora.org/external/imx/imx-manifest]

 

오오 i.MX 8M Plus!!

Cortex-A / GPU / NPU 오오오...

[링크 : https://www.nxp.com/docs/en/user-guide/IMX-MACHINE-LEARNING-UG.pdf]

 

 

+

이미지 받아보니 아래와 같이 구성되어 있다.

귀찮으면 fsl-image-validation-imx-imx8mmevk.sdcard 를 sd에 구워서 켜보면 될 듯.

 

imx_m4_demos에는 bin 파일이 있는데 이건 어떻게 올려서 쓰려나?

 

MCUXpresso 안쓰면 uboot에서 해당 파일을 직접 sd에 넣어 실행하는 수 밖에 없나?

4.2 Run applications using U-Boot

This section describes how to run applications using an SD card and pre-built U-Boot image for i.MX processor.
  1. Following the steps from section 2—Embedded Linux of this Getting Started guide, prepare an SD card with a pre-built U-Boot + Linux image from the Linux BSP package for the i.MX 8M Plus processor. If you have already loaded the SD card with a Linux image, you can skip this step.
  2. Insert the SD card in the host computer (Linux or Windows) and copy the application image (for example hello_world.bin) to the FAT partition of the SD card.
  3. Safely remove the SD card from the PC.
  4. Insert the SD card to the target board. Make sure to use the default boot SD slot and double check the Boot switch setup.
  5. Connect the DEBUG UART connector on the board to the PC through USB cable. The Windows OS installs the USB driver automatically, and the Ubuntu OS will find the serial devices as well.
    See Connect USB debug cable section in Out of box for more instructions on serial communication applications.
  6. Open a second terminal on the i.MX8M Plus EVK board’s second enumerated serial port. This is the Cortex®-M7’s serial console. Set the speed to 115200 bit/s, data bits 8, 1 stop bit (115200, 8N1), no parity.
  7. Power up the board and stop the boot process by pressing any key before the U-Boot countdown reaches zero. At the U-Boot prompt on the first terminal, type the following commands.
    => fatload mmc 0:1 0x48000000 hello_world.bin
    => cp.b 0x48000000 0x7e0000 0x20000
    => bootaux 0x7e0000
    These commands copy the image file from the first partition of the SD card into the Cortex®-M7’s TCM and releases the Cortex®-M7 from reset.

 

 

리눅스에서 /sys 등으로 접근할 순 없나?

wic 파일을 win32diskimager로 구우면 되려나?

[링크 : https://www.nxp.com/docs/en/user-guide/IMX_LINUX_USERS_GUIDE.pdf]

[링크 : https://www.nxp.com/part/8MPLUSLPD4-EVK#/]

 

MCUXpresso 로 imx8m quad 선택해서 빌드한다?

[링크 : https://www.embeddedartists.com/wp-content/uploads/2019/03/iMX8M_Working_with_Cortex-M.pdf]

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