ルールベース画像処理のススメ

データ分析LT会第二回で発表した際の資料です。
youtube: https://www.youtube.com/watch?v=jDZwX3jxhK4

conppass url: https://kaggle-friends.connpass.com/event/214854/

github repository: https://github.com/fkubota/bunseki_compe_LT_02

Transcript

1. ϧʔϧϕʔεը૾ॲཧͷεεϝ
   fkubota
   DeepLearning ωΠςΟϒੈ୅΁޲͚ͯ
   

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2. ࣗݾ঺հ 02
   fkubota (Twitter, Kaggle)
   - όϯυϧΧʔυͷձࣾ(ΧϯϜ)ͰػցֶशΤϯδχΞ
   - Kaggle Expert
   - ԭೄग़਎(౦ژʹग़͖ͯͯ3೥൒)
   - ෺ཧֶՊग़਎(ڧ૬ؔిࢠܥɺ4.2KͷӷମϔϦ΢Ϝʹ࣓ੑମಥͬࠐΜͰͨ)
   - ϓϩάϥϛϯάྺ͸2೥൒͙Β͍
   - झຯ
   - ૣى͖(4࣌൒ىচ)
   - Kaggle
   - ίʔώʔɺϏʔϧɺ΢ΟεΩʔ
   - ಡॻɺ෺ཧɺ఩ֶ
   

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3. ͬͦ͘͞Ͱ͕͢
   ͋Δݐ෺ͷϑϩΞը૾
   ͜Εʹ ͜Μͳײ͡Ͱ੺͍఺Λଧ͍ͪͨ
   - ੨͍ྖҬʹೖͬͪΌͩΊ
   - ௨࿏͚ͩ
   Έͳ͞ΜͳΒͲ͏͠·͔͢ʁ
   03
   

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4. ൃදͷϞνϕʔγϣϯ
   ࣮͸͜ͷը૾ ͸Indoorίϯϖ ͷը૾Ͱ
   ੺͍఺ΛଧͭͨΊʹ؆୯ͳը૾ॲཧΛͯ͠ଧͪ·ͨ͠
   νʔϜϝΠτʹղઆ͢Δͱ൓ԠΑ͔ͬͨͷͰ
   ϧʔϧϕʔεը૾ॲཧʹ͍ͭͯൃද͠Α͏ͱࢥ͍·ͨ͠
   KaggleΛ͖͔͚ͬʹը૾ॲཧɾը૾ೝࣝΛ͸͡Ίͨਓ͸͍͖ͳΓ
   DeepLearning͔Βೖͬͨͱࢥ͍·͕͢ྺ࢙తʹ͸ϧʔϧϕʔε͸ͷ΄͏
   ͕௕͘ɺະͩ׆༂͍ͯ͠Δٕज़Ͱ͢ɻ
   ֶΜͰଛ͸ͳ͍͸ͣʂʂ
   04
   

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5. ࠓ೔࿩͢͜ͱ σΟʔϓϥʔχϯάωΠςΟϒੈ୅ʹ޲͚ͯʂ
   05
   ը૾ͬͯͳʹʁ ը૾ॲཧೖ໳ Indoorͷ৔߹
   ͦ΋ͦ΋ը૾ͬͯͳʹ͔Λ੔
   ཧ͠·͢ɻ
   ϧʔϧϕʔεը૾ॲཧʹͪΐͬ
   ͱ͚ͩೖ໳ͯ͠Έ·͢ɻ
   opencvΛ࢖͍·͢ɻ
   https://github.com/fkubota/
   bunseki_compe_LT_02/blob/main/
   notebook/nb01_opencv.ipynb
   IndoorͰ΍ͬͨ͜ͱΛ΄Μͷগ
   ͠঺հ͠·͢ɻ
   

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6. ը૾ͬͯԿʁ
   

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7. ը૾Λ؆୯ʹ
   - pixel ͱ͍͏࠷খ୯ҐΛ΋ͭ
   - ࠲ඪx, y Λ΋ͭ
   - ً౓஋Λ΋ͭ
   - ௨ৗ͸8bit
   - 0~255ͷ஋ΛऔΔ(256ஈ֊)
   - 0͕ࠇ
   - 255͕ന
   y
   x
   നͬΆ͍ͷͰ
   255ʹ͍ۙ஋
   ࠇͳͷͰ0ʹ͍ۙ஋
   փ৭ͳͷͰਅΜதͷ
   150͙Β͍ʁ
   ΧϥʔͩͱRGBͷ3͕ͭͦΕͧΕ8bitͷ৘ใΛ࣋ͭ
   ྫ:
   - (133, 30, 88) ←10ਐ਺
   - #56a53f ←16ਐ਺
   07
   

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8. ը૾ॲཧೖ໳
   

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9. ϧʔϧϕʔεͷ͍͍ͱ͜Ζ
   ڭࢣσʔλ͍Βͳ͍ ָ͍͠ʂ
   - ΍ͬͨΒΘ͔Δʂʂʂʂ
   - ͜Εҙ֎ͱݟམͱ͞Ε͕ͪ
   09
   

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10. ໰1
    from fkubota shibutsu dataset
    Έͳ͞ΜͳΒͲ͏͠·͔͢ʁ
    (ڭࢣσʔλͳ͠Ͱ͢Αʁ)
    Ωϟοϓͷ࠲ඪΛऔಘ͍ͯͩ͘͠͞
    ༡ͼײ֮Ͱʂʂ
    10
    https://github.com/fkubota/
    bunseki_compe_LT_02/blob/main/
    notebook/nb01_opencv.ipynb
    શ෦githubʹ͋ΔΑ
    

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11. ղ๏ͷ1ྫΛ঺հ
    ·ͣ͸ఆ൪ͷ2஋ԽΛ͢ΔͨΊʹ
    άϨʔը૾ʹม׵͠·͢ʂ
    ֤pixelͷً౓஋(r, g, b) ͷ3࣍ݩ͕
    1࣍ݩʹͳΓ͍Ζ͍Ζѻ͍΍͘͢ͳΓ·͢ɻ
    11
    

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12. 2஋Խ
    എܠͱ෺ମΛ෼͚·͠ΐ͏
    ً౓஋ͷώετάϥϜΛݟͯΈΔͱ
    - നͬΆ͍৭(255ʹ͍ۙ৭)͕ώετάϥ
    ϜͷϚδϣϦςΟʹͳͬͯΔ
    - ͜Ε͸എܠ͕ը૾ͷେ෦෼Λ઎ΊΔ͜
    ͱʹىҼ͢Δ
    - threshold = 70 ͷલޙͰ2஋Խ͢Δʂ
    1 0
    Ωϟοϓ
    എܠ
    12
    

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13. 2஋Խ݁Ռ
    ϐϯΫͷ෦෼͕1
    ͦΕҙ֎͕0
    ͷ ΦϒδΣΫτ ͕Ͱ͖ͨ
    ͜ΕΛ “ྖҬ” ͱݴ͍·͢
    13
    

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14. ͋ͱ͸៉ྷʹ͢Δ
    ࠓճ͸݀ΛຒΊ͚ͨͩ
    14
    

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15. ॏ৺Λܭࢉ
    ྖҬͷྠֲΛऔͬͯॏ৺Λܭࢉ
    Ͱ͖ͨʂʂ
    15
    

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16. ໰2
    from fkubota shibutsu dataset
    Ωϟοϓͷ࠲ඪΛऔಘ͍ͯͩ͘͠͞
    ָ͘͠ͳ͖ͬͯ·ͨ͠ʁ
    16
    

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17. Կ͕೉͍͠ʁ
    Ωϟοϓͷ࠲ඪΛऔಘ͍ͯͩ͘͠͞
    ಉ͡໰୊ͳΜ͚ͩͲෳ਺෺ମ͕͋ΔͷͰ
    ೉қ౓͕௓Ͷ্͕Γ·͢Ͷɻ
    17
    

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18. ఆ൪ͷॲཧ
    άϨʔը૾ 2஋Խ
    18
    

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19. νϣοτ͖Ε͍ʹ
    ϞϧϑΥϩδʔม׵
    - ࣌ؒͷ౎߹্ৄࡉ͸ল͖·͢
    - ϊΠζআڈɺ݀ຒΊ͕Ͱ͖·͢
    - ݪཧ͸؆୯ͳͷͰͥͻௐ΂ͯΈͯ
    ͍ͩ͘͞
    ݀ຒΊ
    ΊͬͪΌ͖Ε͍ʹͳͬͨʂʂ
    19
    

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20. ϥϕϦϯά
    ࿈ଓͨ͠ΦϒδΣΫτຖʹ෼ׂ
    - ྡΓ߹ͬͨ1ΛಉҰͷϥϕϧͱͯ͠෼ׂ͢Δ
    - ϐϯΫͷྖҬ͕ෳ਺ͷྖҬʹ෼ׂ͞Εͨ
    - ෼ׂޙ໘ੵ͕খ͍͞΋ͷΛϊΠζͱͯ͠আ͍ͨ
    ໘ੵͷখ͍͞ྖҬ(ϊΠζ)
    20
    

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21. ͓ΘΓ͡Όͳ͍Αʂ
    ͜ͷத͔ΒΩϟοϓΛબͼ
    ग़͞ͳ͍ͱ͍͚·ͤΜ
    ಛ௃ྔΛ࢖͍·͢
    ͜ͷ5ͭͷ෺ମΛൺֱͯ͠Ωϟοϓʹ͸Ͳ͏
    ͍ͬͨಛ௃͕͋ΔͰ͠ΐ͏͔ʁ
    - ؙΈ͕͋Δɹ
    - ໘ੵ͕খ͍͞
    ͕ࢥ͍౰ͨΓ·͢ɻ
    ໘ੵ͕খ͍͞Λ࢖ͬͯΈ·͢ɻ
    ͭ·Γ
    ಛ௃ྔʹ໘ੵΛ࢖͏ͱ͍͏͜ͱʹͳΓ·͢ɻ
    21
    

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22. ྖҬͷ໘ੵΛܭࢉ
    ໘ੵ࠷খΛબͿ
    ಛ௃ྔ = ໘ੵ
    Ͱ͖ͨʂʂ
    22
    

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23. ໰3 Ͱ͖ͦʁ
    from fkubota shibutsu dataset
    ϖϯͷ࠲ඪΛऔಘ͍ͯͩ͘͠͞
    23
    

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24. ϥϕϦϯά·Ͱ͸Ұॹ
    ϖϯͷಛ௃Λߟ͑Δ
    fkubota͞Μ͸ɺʮࡉ௕͍ʯҙ֎
    ࢥ͍͖ͭ·ͤΜͰͨ͠ɻ
    ࡉ௕͍͸͜͏දݱ͢Δ͜ͱʹ͠·͢ɻ
    1. ྖҬΛ࠷খ֎઀ۣܗͰғ͏
    2. ʮ௕ลͷ௕͞ʯΛʮ୹ลͷ௕͞ʯͰׂΔ
    3. ͜ΕΛʮࡉ௕͞౓ʯͱ͢Δ
    24
    

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25. ࡉ௕͍౓Λܭࢉ
    ಛ௃ྔ = ࡉ௕͍౓ ࠷େ஋ΛબͿ
    Ͱ͖ͨʂʂ
    25
    

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26. ໰4 ࠷ޙͩΑʂ
    from fkubota shibutsu dataset
    MINTIAͷ࠲ඪΛऔಘ͍ͯͩ͘͠͞
    26
    

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27. ಛ௃͸ʁ
    MINTIAͷಛ௃Λߟ͑Δ
    ࢛͍֯ͱ͍͏ಛ௃͕͋Γ·͕͢ɺ
    ଞͷ෺ମͱࠩผԽ͕೉͍͠Ͱ͢ɻ
    ܗͷ؍఺Ͱ͸೉ͦ͠͏ͳͷͰ
    ʮ৭ʯͰ߈ΊͯΈ·͠ΐ͏ɻ
    ʮ੨͞ʯΛදݱ͠·͢ɻ
    RGBΛHSVʹม׵͠
    H(hue, ৭૬)ͷը૾Λ࢖͍·͢ɻ
    27
    

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28. ৭૬ʁ
    ͜ͷล͕੨
    ৭૬͸৭ͷ༷૬Λ
    0~180·ͰͰมԽͤͨ͞΋ͷɻ
    ੨͸100~140͋ͨΓɻ
    28
    

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29. Hueը૾
    hueը૾
    ͜ͷลΓ
    29
    

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30. 2஋Խͯ͠ϞϧϑΥϩδʔ
    2஋Խ ϞϧϑΥϩδʔ
    30
    

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31. ॏ৺Λܭࢉͯ͠ऴΘΓʂ
    Ͱ͖ͨʂʂ
    31
    

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32. Indoorͷ৔߹
    

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33. ࠷ॳͷ࣭໰
    ͋Δݐ෺ͷϑϩΞը૾
    ͜Εʹ ͜Μͳײ͡Ͱ੺͍఺Λଧ͍ͪͨ
    - ੨͍ྖҬʹೖͬͪΌͩΊ
    - ௨࿏͚ͩ
    Έͳ͞ΜͳΒͲ͏͠·͔͢ʁ
    33
    

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34. ·ͣ͸௨࿏Λऔಘ
    ੨͍෦෼Λ2஋Խ
    ຒΊΔ
    ന͍෦෼Λ
    2஋Խ
    ࿦ཧੵ
    34
    

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35. ׬੒ʂ
    &
    ੺͍఺ʑΛॻ͍ͨը૾Λ༻ҙͯ͠
    ࿦ཧੵ
    Ͱ͖ͨʂʂ
    35
    

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36. Thanks :)
    

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