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• FFMPEG Output File is Empty Nothing was Encoded (for a Picture) ?

  4 mars 2023, par Sarah Szabo

  I have a strange issue effecting one of my programs that does bulk media conversions using ffmpeg from the command line, however this effects me using it directly from the shell as well :

  


  ffmpeg -i INPUT.mkv -ss 0:30 -y -qscale:v 2 -frames:v 1 -f image2 -huffman optimal "OUTPUT.png"
fails every run with the error message :
Output file is empty, nothing was encoded (check -ss / -t / -frames parameters if used)

  


  This only happens with very specific videos, and seemingly no other videos. File type is usually .webm. These files have been downloaded properly (usually from yt-dlp), and I have tried re-downloading them just to verify their integrity.

  


  One such file from a colleague was : https://www.dropbox.com/s/xkucr2z5ra1p2oh/Triggerheart%20Execlica%20OST%20%28Arrange%29%20-%20Crueltear%20Ending.mkv?dl=0

  


  Is there a subtle issue with the command string ?

  


  Notes :

  


  removing -huffman optimal had no effect

  


  moving -ss to before -i had no effect

  


  removing -f image2 had no effect

  


  Full Log :

  


  sarah@MidnightStarSign:~/Music/Playlists/Indexing/Indexing Temp$ ffmpeg -i Triggerheart\ Execlica\ OST\ \(Arrange\)\ -\ Crueltear\ Ending.mkv -ss 0:30 -y -qscale:v 2 -frames:v 1 -f image2 -huffman optimal "TEST.png"
ffmpeg version n5.1.2 Copyright (c) 2000-2022 the FFmpeg developers
  built with gcc 12.2.0 (GCC)
  configuration: --prefix=/usr --disable-debug --disable-static --disable-stripping --enable-amf --enable-avisynth --enable-cuda-llvm --enable-lto --enable-fontconfig --enable-gmp --enable-gnutls --enable-gpl --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libdav1d --enable-libdrm --enable-libfreetype --enable-libfribidi --enable-libgsm --enable-libiec61883 --enable-libjack --enable-libmfx --enable-libmodplug --enable-libmp3lame --enable-libopencore_amrnb --enable-libopencore_amrwb --enable-libopenjpeg --enable-libopus --enable-libpulse --enable-librav1e --enable-librsvg --enable-libsoxr --enable-libspeex --enable-libsrt --enable-libssh --enable-libsvtav1 --enable-libtheora --enable-libv4l2 --enable-libvidstab --enable-libvmaf --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxcb --enable-libxml2 --enable-libxvid --enable-libzimg --enable-nvdec --enable-nvenc --enable-opencl --enable-opengl --enable-shared --enable-version3 --enable-vulkan
  libavutil      57. 28.100 / 57. 28.100
  libavcodec     59. 37.100 / 59. 37.100
  libavformat    59. 27.100 / 59. 27.100
  libavdevice    59.  7.100 / 59.  7.100
  libavfilter     8. 44.100 /  8. 44.100
  libswscale      6.  7.100 /  6.  7.100
  libswresample   4.  7.100 /  4.  7.100
  libpostproc    56.  6.100 / 56.  6.100
[matroska,webm @ 0x55927f484740] Could not find codec parameters for stream 2 (Attachment: none): unknown codec
Consider increasing the value for the 'analyzeduration' (0) and 'probesize' (5000000) options
Input #0, matroska,webm, from 'Triggerheart Execlica OST (Arrange) - Crueltear Ending.mkv':
  Metadata:
    title           : TriggerHeart Exelica PS2 & 360 Arrange ー 16 - Crueltear Ending
    PURL            : https://www.youtube.com/watch?v=zJ0bEa_8xEg
    COMMENT         : https://www.youtube.com/watch?v=zJ0bEa_8xEg
    ARTIST          : VinnyVynce
    DATE            : 20170905
    ENCODER         : Lavf59.27.100
  Duration: 00:00:30.00, start: -0.007000, bitrate: 430 kb/s
  Stream #0:0(eng): Video: vp9 (Profile 0), yuv420p(tv, bt709), 720x720, SAR 1:1 DAR 1:1, 25 fps, 25 tbr, 1k tbn (default)
    Metadata:
      DURATION        : 00:00:29.934000000
  Stream #0:1(eng): Audio: opus, 48000 Hz, stereo, fltp (default)
    Metadata:
      DURATION        : 00:00:30.001000000
  Stream #0:2: Attachment: none
    Metadata:
      filename        : cover.webp
      mimetype        : image/webp
Codec AVOption huffman (Huffman table strategy) specified for output file #0 (TEST.png) has not been used for any stream. The most likely reason is either wrong type (e.g. a video option with no video streams) or that it is a private option of some encoder which was not actually used for any stream.
Stream mapping:
  Stream #0:0 -> #0:0 (vp9 (native) -> png (native))
Press [q] to stop, [?] for help
Output #0, image2, to 'TEST.png':
  Metadata:
    title           : TriggerHeart Exelica PS2 & 360 Arrange ー 16 - Crueltear Ending
    PURL            : https://www.youtube.com/watch?v=zJ0bEa_8xEg
    COMMENT         : https://www.youtube.com/watch?v=zJ0bEa_8xEg
    ARTIST          : VinnyVynce
    DATE            : 20170905
    encoder         : Lavf59.27.100
  Stream #0:0(eng): Video: png, rgb24, 720x720 [SAR 1:1 DAR 1:1], q=2-31, 200 kb/s, 25 fps, 25 tbn (default)
    Metadata:
      DURATION        : 00:00:29.934000000
      encoder         : Lavc59.37.100 png
frame=    0 fps=0.0 q=0.0 Lsize=N/A time=00:00:00.00 bitrate=N/A speed=   0x    
video:0kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: unknown
Output file is empty, nothing was encoded (check -ss / -t / -frames parameters if used)


  


  Manjaro OS System Specs :

  


  System:&#xA;  Kernel: 6.1.12-1-MANJARO arch: x86_64 bits: 64 compiler: gcc v: 12.2.1&#xA;    parameters: BOOT_IMAGE=/@/boot/vmlinuz-6.1-x86_64&#xA;    root=UUID=f11386cf-342d-47ac-84e6-484b7b2f377d rw rootflags=subvol=@&#xA;    radeon.modeset=1 nvdia-drm.modeset=1 quiet&#xA;    cryptdevice=UUID=059df4b4-5be4-44d6-a23a-de81135eb5b4:luks-disk&#xA;    root=/dev/mapper/luks-disk apparmor=1 security=apparmor&#xA;    resume=/dev/mapper/luks-swap udev.log_priority=3&#xA;  Desktop: KDE Plasma v: 5.26.5 tk: Qt v: 5.15.8 wm: kwin_x11 vt: 1 dm: SDDM&#xA;    Distro: Manjaro Linux base: Arch Linux&#xA;Machine:&#xA;  Type: Desktop Mobo: ASUSTeK model: PRIME X570-PRO v: Rev X.0x&#xA;    serial: <superuser required="required"> UEFI: American Megatrends v: 4408&#xA;    date: 10/27/2022&#xA;Battery:&#xA;  Message: No system battery data found. Is one present?&#xA;Memory:&#xA;  RAM: total: 62.71 GiB used: 27.76 GiB (44.3%)&#xA;  RAM Report: permissions: Unable to run dmidecode. Root privileges required.&#xA;CPU:&#xA;  Info: model: AMD Ryzen 9 5950X bits: 64 type: MT MCP arch: Zen 3&#x2B; gen: 4&#xA;    level: v3 note: check built: 2022 process: TSMC n6 (7nm) family: 0x19 (25)&#xA;    model-id: 0x21 (33) stepping: 0 microcode: 0xA201016&#xA;  Topology: cpus: 1x cores: 16 tpc: 2 threads: 32 smt: enabled cache:&#xA;    L1: 1024 KiB desc: d-16x32 KiB; i-16x32 KiB L2: 8 MiB desc: 16x512 KiB&#xA;    L3: 64 MiB desc: 2x32 MiB&#xA;  Speed (MHz): avg: 4099 high: 4111 min/max: 2200/6358 boost: disabled&#xA;    scaling: driver: acpi-cpufreq governor: schedutil cores: 1: 4099 2: 4095&#xA;    3: 4102 4: 4100 5: 4097 6: 4100 7: 4110 8: 4111 9: 4083 10: 4099 11: 4100&#xA;    12: 4094 13: 4097 14: 4101 15: 4100 16: 4099 17: 4100 18: 4097 19: 4098&#xA;    20: 4095 21: 4100 22: 4099 23: 4099 24: 4105 25: 4098 26: 4100 27: 4100&#xA;    28: 4092 29: 4103 30: 4101 31: 4100 32: 4099 bogomips: 262520&#xA;  Flags: 3dnowprefetch abm adx aes aperfmperf apic arat avic avx avx2 bmi1&#xA;    bmi2 bpext cat_l3 cdp_l3 clflush clflushopt clwb clzero cmov cmp_legacy&#xA;    constant_tsc cpb cpuid cqm cqm_llc cqm_mbm_local cqm_mbm_total&#xA;    cqm_occup_llc cr8_legacy cx16 cx8 de decodeassists erms extapic&#xA;    extd_apicid f16c flushbyasid fma fpu fsgsbase fsrm fxsr fxsr_opt ht&#xA;    hw_pstate ibpb ibrs ibs invpcid irperf lahf_lm lbrv lm mba mca mce&#xA;    misalignsse mmx mmxext monitor movbe msr mtrr mwaitx nonstop_tsc nopl npt&#xA;    nrip_save nx ospke osvw overflow_recov pae pat pausefilter pclmulqdq&#xA;    pdpe1gb perfctr_core perfctr_llc perfctr_nb pfthreshold pge pku pni popcnt&#xA;    pse pse36 rapl rdpid rdpru rdrand rdseed rdt_a rdtscp rep_good sep sha_ni&#xA;    skinit smap smca smep ssbd sse sse2 sse4_1 sse4_2 sse4a ssse3 stibp succor&#xA;    svm svm_lock syscall tce topoext tsc tsc_scale umip v_spec_ctrl&#xA;    v_vmsave_vmload vaes vgif vmcb_clean vme vmmcall vpclmulqdq wbnoinvd wdt&#xA;    x2apic xgetbv1 xsave xsavec xsaveerptr xsaveopt xsaves&#xA;  Vulnerabilities:&#xA;  Type: itlb_multihit status: Not affected&#xA;  Type: l1tf status: Not affected&#xA;  Type: mds status: Not affected&#xA;  Type: meltdown status: Not affected&#xA;  Type: mmio_stale_data status: Not affected&#xA;  Type: retbleed status: Not affected&#xA;  Type: spec_store_bypass mitigation: Speculative Store Bypass disabled via&#xA;    prctl&#xA;  Type: spectre_v1 mitigation: usercopy/swapgs barriers and __user pointer&#xA;    sanitization&#xA;  Type: spectre_v2 mitigation: Retpolines, IBPB: conditional, IBRS_FW,&#xA;    STIBP: always-on, RSB filling, PBRSB-eIBRS: Not affected&#xA;  Type: srbds status: Not affected&#xA;  Type: tsx_async_abort status: Not affected&#xA;Graphics:&#xA;  Device-1: NVIDIA GA104 [GeForce RTX 3070] vendor: ASUSTeK driver: nvidia&#xA;    v: 525.89.02 alternate: nouveau,nvidia_drm non-free: 525.xx&#x2B;&#xA;    status: current (as of 2023-02) arch: Ampere code: GAxxx&#xA;    process: TSMC n7 (7nm) built: 2020-22 pcie: gen: 4 speed: 16 GT/s lanes: 8&#xA;    link-max: lanes: 16 bus-ID: 0b:00.0 chip-ID: 10de:2484 class-ID: 0300&#xA;  Device-2: AMD Cape Verde PRO [Radeon HD 7750/8740 / R7 250E]&#xA;    vendor: VISIONTEK driver: radeon v: kernel alternate: amdgpu arch: GCN-1&#xA;    code: Southern Islands process: TSMC 28nm built: 2011-20 pcie: gen: 3&#xA;    speed: 8 GT/s lanes: 8 link-max: lanes: 16 ports: active: DP-3,DP-4&#xA;    empty: DP-1, DP-2, DP-5, DP-6 bus-ID: 0c:00.0 chip-ID: 1002:683f&#xA;    class-ID: 0300 temp: 54.0 C&#xA;  Device-3: Microdia USB 2.0 Camera type: USB driver: snd-usb-audio,uvcvideo&#xA;    bus-ID: 9-2:3 chip-ID: 0c45:6367 class-ID: 0102 serial: <filter>&#xA;  Display: x11 server: X.Org v: 21.1.7 with: Xwayland v: 22.1.8&#xA;    compositor: kwin_x11 driver: X: loaded: modesetting,nvidia dri: radeonsi&#xA;    gpu: radeon display-ID: :0 screens: 1&#xA;  Screen-1: 0 s-res: 5760x2160 s-dpi: 80 s-size: 1829x686mm (72.01x27.01")&#xA;    s-diag: 1953mm (76.91")&#xA;  Monitor-1: DP-1 pos: 1-2 res: 1920x1080 dpi: 93&#xA;    size: 527x296mm (20.75x11.65") diag: 604mm (23.8") modes: N/A&#xA;  Monitor-2: DP-1-3 pos: 2-1 res: 1920x1080 dpi: 82&#xA;    size: 598x336mm (23.54x13.23") diag: 686mm (27.01") modes: N/A&#xA;  Monitor-3: DP-1-4 pos: 1-1 res: 1920x1080 dpi: 93&#xA;    size: 527x296mm (20.75x11.65") diag: 604mm (23.8") modes: N/A&#xA;  Monitor-4: DP-3 pos: primary,2-2 res: 1920x1080 dpi: 82&#xA;    size: 598x336mm (23.54x13.23") diag: 686mm (27.01") modes: N/A&#xA;  Monitor-5: DP-4 pos: 2-4 res: 1920x1080 dpi: 82&#xA;    size: 598x336mm (23.54x13.23") diag: 686mm (27.01") modes: N/A&#xA;  Monitor-6: HDMI-0 pos: 1-3 res: 1920x1080 dpi: 93&#xA;    size: 527x296mm (20.75x11.65") diag: 604mm (23.8") modes: N/A&#xA;  API: OpenGL v: 4.6.0 NVIDIA 525.89.02 renderer: NVIDIA GeForce RTX&#xA;    3070/PCIe/SSE2 direct-render: Yes&#xA;Audio:&#xA;  Device-1: NVIDIA GA104 High Definition Audio vendor: ASUSTeK&#xA;    driver: snd_hda_intel bus-ID: 5-1:2 v: kernel chip-ID: 30be:1019 pcie:&#xA;    class-ID: 0102 gen: 4 speed: 16 GT/s lanes: 8 link-max: lanes: 16&#xA;    bus-ID: 0b:00.1 chip-ID: 10de:228b class-ID: 0403&#xA;  Device-2: AMD Oland/Hainan/Cape Verde/Pitcairn HDMI Audio [Radeon HD 7000&#xA;    Series] vendor: VISIONTEK driver: snd_hda_intel v: kernel pcie: gen: 3&#xA;    speed: 8 GT/s lanes: 8 link-max: lanes: 16 bus-ID: 0c:00.1&#xA;    chip-ID: 1002:aab0 class-ID: 0403&#xA;  Device-3: AMD Starship/Matisse HD Audio vendor: ASUSTeK&#xA;    driver: snd_hda_intel v: kernel pcie: gen: 4 speed: 16 GT/s lanes: 16&#xA;    bus-ID: 0e:00.4 chip-ID: 1022:1487 class-ID: 0403&#xA;  Device-4: Schiit Audio Unison Universal Dac type: USB driver: snd-usb-audio&#xA;  Device-5: JMTek LLC. Plugable USB Audio Device type: USB&#xA;    driver: hid-generic,snd-usb-audio,usbhid bus-ID: 5-2:3 chip-ID: 0c76:120b&#xA;    class-ID: 0300 serial: <filter>&#xA;  Device-6: ASUSTek ASUS AI Noise-Cancelling Mic Adapter type: USB&#xA;    driver: hid-generic,snd-usb-audio,usbhid bus-ID: 5-4:4 chip-ID: 0b05:194e&#xA;    class-ID: 0300 serial: <filter>&#xA;  Device-7: Microdia USB 2.0 Camera type: USB driver: snd-usb-audio,uvcvideo&#xA;    bus-ID: 9-2:3 chip-ID: 0c45:6367 class-ID: 0102 serial: <filter>&#xA;  Sound API: ALSA v: k6.1.12-1-MANJARO running: yes&#xA;  Sound Interface: sndio v: N/A running: no&#xA;  Sound Server-1: PulseAudio v: 16.1 running: no&#xA;  Sound Server-2: PipeWire v: 0.3.65 running: yes&#xA;Network:&#xA;  Device-1: Intel I211 Gigabit Network vendor: ASUSTeK driver: igb v: kernel&#xA;    pcie: gen: 1 speed: 2.5 GT/s lanes: 1 port: f000 bus-ID: 07:00.0&#xA;    chip-ID: 8086:1539 class-ID: 0200&#xA;  IF: enp7s0 state: up speed: 1000 Mbps duplex: full mac: <filter>&#xA;  IP v4: <filter> type: dynamic noprefixroute scope: global&#xA;    broadcast: <filter>&#xA;  IP v6: <filter> type: noprefixroute scope: link&#xA;  IF-ID-1: docker0 state: down mac: <filter>&#xA;  IP v4: <filter> scope: global broadcast: <filter>&#xA;  WAN IP: <filter>&#xA;Bluetooth:&#xA;  Device-1: Cambridge Silicon Radio Bluetooth Dongle (HCI mode) type: USB&#xA;    driver: btusb v: 0.8 bus-ID: 5-5.3:7 chip-ID: 0a12:0001 class-ID: e001&#xA;  Report: rfkill ID: hci0 rfk-id: 0 state: up address: see --recommends&#xA;Logical:&#xA;  Message: No logical block device data found.&#xA;  Device-1: luks-c847cf9f-c6b5-4624-a25e-4531e318851a maj-min: 254:2&#xA;    type: LUKS dm: dm-2 size: 3.64 TiB&#xA;  Components:&#xA;  p-1: sda1 maj-min: 8:1 size: 3.64 TiB&#xA;  Device-2: luks-swap maj-min: 254:1 type: LUKS dm: dm-1 size: 12 GiB&#xA;  Components:&#xA;  p-1: nvme0n1p2 maj-min: 259:2 size: 12 GiB&#xA;  Device-3: luks-disk maj-min: 254:0 type: LUKS dm: dm-0 size: 919.01 GiB&#xA;  Components:&#xA;  p-1: nvme0n1p3 maj-min: 259:3 size: 919.01 GiB&#xA;RAID:&#xA;  Message: No RAID data found.&#xA;Drives:&#xA;  Local Storage: total: 9.1 TiB used: 2.79 TiB (30.6%)&#xA;  SMART Message: Unable to run smartctl. Root privileges required.&#xA;  ID-1: /dev/nvme0n1 maj-min: 259:0 vendor: Western Digital&#xA;    model: WDS100T3X0C-00SJG0 size: 931.51 GiB block-size: physical: 512 B&#xA;    logical: 512 B speed: 31.6 Gb/s lanes: 4 type: SSD serial: <filter>&#xA;    rev: 111110WD temp: 53.9 C scheme: GPT&#xA;  ID-2: /dev/nvme1n1 maj-min: 259:4 vendor: Western Digital&#xA;    model: WDS100T2B0C-00PXH0 size: 931.51 GiB block-size: physical: 512 B&#xA;    logical: 512 B speed: 31.6 Gb/s lanes: 4 type: SSD serial: <filter>&#xA;    rev: 211070WD temp: 46.9 C scheme: GPT&#xA;  ID-3: /dev/sda maj-min: 8:0 vendor: Western Digital&#xA;    model: WD4005FZBX-00K5WB0 size: 3.64 TiB block-size: physical: 4096 B&#xA;    logical: 512 B speed: 6.0 Gb/s type: HDD rpm: 7200 serial: <filter>&#xA;    rev: 1A01 scheme: GPT&#xA;  ID-4: /dev/sdb maj-min: 8:16 vendor: Western Digital&#xA;    model: WD4005FZBX-00K5WB0 size: 3.64 TiB block-size: physical: 4096 B&#xA;    logical: 512 B speed: 6.0 Gb/s type: HDD rpm: 7200 serial: <filter>&#xA;    rev: 1A01 scheme: GPT&#xA;  ID-5: /dev/sdc maj-min: 8:32 type: USB vendor: SanDisk&#xA;    model: Gaming Xbox 360 size: 7.48 GiB block-size: physical: 512 B&#xA;    logical: 512 B type: N/A serial: <filter> rev: 8.02 scheme: MBR&#xA;  SMART Message: Unknown USB bridge. 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• Developing MobyCAIRO

  26 mai 2021, par Multimedia Mike — General

  I recently published a tool called MobyCAIRO. The ‘CAIRO’ part stands for Computer-Assisted Image ROtation, while the ‘Moby’ prefix refers to its role in helping process artifact image scans to submit to the MobyGames database. The tool is meant to provide an accelerated workflow for rotating and cropping image scans. It works on both Windows and Linux. Hopefully, it can solve similar workflow problems for other people.

  As of this writing, MobyCAIRO has not been tested on Mac OS X yet– I expect some issues there that should be easily solvable if someone cares to test it.

  The rest of this post describes my motivations and how I arrived at the solution.

  Background
  I have scanned well in excess of 2100 images for MobyGames and other purposes in the past 16 years or so. The workflow looks like this :

  
  

  

  Image workflow

  
  

  It should be noted that my original workflow featured me manually rotating the artifact on the scanner bed in order to ensure straightness, because I guess I thought that rotate functions in image editing programs constituted dark, unholy magic or something. So my workflow used to be even more arduous :

  
  

  

  I can’t believe I had the patience to do this for hundreds of scans

  
  

  Sometime last year, I was sitting down to perform some more scanning and found myself dreading the oncoming tedium of straightening and cropping the images. This prompted a pivotal question :

  
  Why can’t a computer do this for me ?
  

  After all, I have always been a huge proponent of making computers handle the most tedious, repetitive, mind-numbing, and error-prone tasks. So I did some web searching to find if there were any solutions that dealt with this. I also consulted with some like-minded folks who have to cope with the same tedious workflow.

  I came up empty-handed. So I endeavored to develop my own solution.

  Problem Statement and Prior Work
  
  I want to develop a workflow that can automatically rotate an image so that it is straight, and also find the most likely crop rectangle, uniformly whitening the area outside of the crop area (in the case of circles).

  As mentioned, I checked to see if any other programs can handle this, starting with my usual workhorse, Photoshop Elements. But I can’t expect the trimmed down version to do everything. I tried to find out if its big brother could handle the task, but couldn’t find a definitive answer on that. Nor could I find any other tools that seem to take an interest in optimizing this particular workflow.

  When I brought this up to some peers, I received some suggestions, including an idea that the venerable GIMP had a feature like this, but I could not find any evidence. Further, I would get responses of “Program XYZ can do image rotation and cropping.” I had to tamp down on the snark to avoid saying “Wow ! An image editor that can perform rotation AND cropping ? What a game-changer !” Rotation and cropping features are table stakes for any halfway competent image editor for the last 25 or so years at least. I am hoping to find or create a program which can lend a bit of programmatic assistance to the task.

  Why can’t other programs handle this ? The answer seems fairly obvious : Image editing tools are general tools and I want a highly customized workflow. It’s not reasonable to expect a turnkey solution to do this.

  Brainstorming An Approach
  I started with the happiest of happy cases— A disc that needed archiving (a marketing/press assets CD-ROM from a video game company, contents described here) which appeared to have some pretty clear straight lines :

  
  
  

  My idea was to try to find straight lines in the image and then rotate the image so that the image is parallel to the horizontal based on the longest single straight line detected.

  I just needed to figure out how to find a straight line inside of an image. Fortunately, I quickly learned that this is very much a solved problem thanks to something called the Hough transform. As a bonus, I read that this is also the tool I would want to use for finding circles, when I got to that part. The nice thing about knowing the formal algorithm to use is being able to find efficient, optimized libraries which already implement it.

  Early Prototype
  A little searching for how to perform a Hough transform in Python led me first to scikit. I was able to rapidly produce a prototype that did some basic image processing. However, running the Hough transform directly on the image and rotating according to the longest line segment discovered turned out not to yield expected results.

  
  
  

  It also took a very long time to chew on the 3300×3300 raw image– certainly longer than I care to wait for an accelerated workflow concept. The key, however, is that you are apparently not supposed to run the Hough transform on a raw image– you need to compute the edges first, and then attempt to determine which edges are ‘straight’. The recommended algorithm for this step is the Canny edge detector. After applying this, I get the expected rotation :

  
  
  

  The algorithm also completes in a few seconds. So this is a good early result and I was feeling pretty confident. But, again– happiest of happy cases. I should also mention at this point that I had originally envisioned a tool that I would simply run against a scanned image and it would automatically/magically make the image straight, followed by a perfect crop.

  Along came my MobyGames comrade Foxhack to disabuse me of the hope of ever developing a fully automated tool. Just try and find a usefully long straight line in this :

  
  

  Darn it, Foxhack…
  

  There are straight edges, to be sure. But my initial brainstorm of rotating according to the longest straight edge looks infeasible. Further, it’s at this point that we start brainstorming that perhaps we could match on ratings badges such as the standard ESRB badges omnipresent on U.S. video games. This gets into feature detection and complicates things.

  This Needs To Be Interactive
  At this point in the effort, I came to terms with the fact that the solution will need to have some element of interactivity. I will also need to get out of my safe Linux haven and figure out how to develop this on a Windows desktop, something I am not experienced with.

  I initially dreamed up an impressive beast of a program written in C++ that leverages Windows desktop GUI frameworks, OpenGL for display and real-time rotation, GPU acceleration for image analysis and processing tricks, and some novel input concepts. I thought GPU acceleration would be crucial since I have a fairly good GPU on my main Windows desktop and I hear that these things are pretty good at image processing.

  I created a list of prototyping tasks on a Trello board and made a decent amount of headway on prototyping all the various pieces that I would need to tie together in order to make this a reality. But it was ultimately slowgoing when you can only grab an hour or 2 here and there to try to get anything done.

  Settling On A Solution
  Recently, I was determined to get a set of old shareware discs archived. I ripped the data a year ago but I was blocked on the scanning task because I knew that would also involve tedious straightening and cropping. So I finally got all the scans done, which was reasonably quick. But I was determined to not manually post-process them.

  This was fairly recent, but I can’t quite recall how I managed to come across the OpenCV library and its Python bindings. OpenCV is an amazing library that provides a significant toolbox for performing image processing tasks. Not only that, it provides “just enough” UI primitives to be able to quickly create a basic GUI for your program, including image display via multiple windows, buttons, and keyboard/mouse input. Furthermore, OpenCV seems to be plenty fast enough to do everything I need in real time, just with (accelerated where appropriate) CPU processing.

  So I went to work porting the ideas from the simple standalone Python/scikit tool. I thought of a refinement to the straight line detector– instead of just finding the longest straight edge, it creates a histogram of 360 rotation angles, and builds a list of lines corresponding to each angle. Then it sorts the angles by cumulative line length and allows the user to iterate through this list, which will hopefully provide the most likely straightened angle up front. Further, the tool allows making fine adjustments by 1/10 of an angle via the keyboard, not the mouse. It does all this while highlighting in red the straight line segments that are parallel to the horizontal axis, per the current candidate angle.

  
  
  

  The tool draws a light-colored grid over the frame to aid the user in visually verifying the straightness of the image. Further, the program has a mode that allows the user to see the algorithm’s detected edges :

  
  
  

  For the cropping phase, the program uses the Hough circle transform in a similar manner, finding the most likely circles (if the image to be processed is supposed to be a circle) and allowing the user to cycle among them while making precise adjustments via the keyboard, again, rather than the mouse.

  
  
  

  Running the Hough circle transform is a significantly more intensive operation than the line transform. When I ran it on a full 3300×3300 image, it ran for a long time. I didn’t let it run longer than a minute before forcibly ending the program. Is this approach unworkable ? Not quite– It turns out that the transform is just as effective when shrinking the image to 400×400, and completes in under 2 seconds on my Core i5 CPU.

  For rectangular cropping, I just settled on using OpenCV’s built-in region-of-interest (ROI) facility. I tried to intelligently find the best candidate rectangle and allow fine adjustments via the keyboard, but I wasn’t having much success, so I took a path of lesser resistance.

  Packaging and Residual Weirdness
  I realized that this tool would be more useful to a broader Windows-using base of digital preservationists if they didn’t have to install Python, establish a virtual environment, and install the prerequisite dependencies. Thus, I made the effort to figure out how to wrap the entire thing up into a monolithic Windows EXE binary. It is available from the project’s Github release page (another thing I figured out for the sake of this project !).

  The binary is pretty heavy, weighing in at a bit over 50 megabytes. You might advise using compression– it IS compressed ! Before I figured out the --onefile command for pyinstaller.exe, the generated dist/ subdirectory was 150 MB. Among other things, there’s a 30 MB FORTRAN BLAS library packaged in !

  Conclusion and Future Directions
  Once I got it all working with a simple tkinter UI up front in order to select between circle and rectangle crop modes, I unleashed the tool on 60 or so scans in bulk, using the Windows forfiles command (another learning experience). I didn’t put a clock on the effort, but it felt faster. Of course, I was livid with proudness the whole time because I was using my own tool. I just wish I had thought of it sooner. But, really, with 2100+ scans under my belt, I’m just getting started– I literally have thousands more artifacts to scan for preservation.

  The tool isn’t perfect, of course. Just tonight, I threw another scan at MobyCAIRO. Just go ahead and try to find straight lines in this specimen :

  
  
  

  I eventually had to use the text left and right of center to line up against the grid with the manual keyboard adjustments. Still, I’m impressed by how these computer vision algorithms can see patterns I can’t, highlighting lines I never would have guessed at.

  I’m eager to play with OpenCV some more, particularly the video processing functions, perhaps even some GPU-accelerated versions.

  The post Developing MobyCAIRO first appeared on Breaking Eggs And Making Omelettes.

• Revision 29748 : Mise à jour de l’ensembles des fonctions ... on peut passer maintenant à ...

  8 juillet 2009, par kent1@… — Log

  Mise à jour de l’ensembles des fonctions ... on peut passer maintenant à l’encodage multiple