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delete mode 100644 resources/smieci w kontenerach/plastic/plastic389.jpg delete mode 100644 resources/smieci w kontenerach/plastic/plastic403.jpg create mode 100644 resources/smieci w kontenerach/plastic/plastic407.jpg delete mode 100644 resources/smieci w kontenerach/plastic/plastic408.jpg create mode 100644 resources/smieci w kontenerach/plastic/plastic472.jpg create mode 100644 resources/smieci w kontenerach/plastic/plastic79.jpg diff --git a/adamORaport.md b/adamORaport.md new file mode 100644 index 0000000..88e5d35 --- /dev/null +++ b/adamORaport.md @@ -0,0 +1,123 @@ +# Sztuczna Inteligencja + +**Temat projektu:** Inteligenta Śmieciarka + +**Zespół:** Kacper Borkowski, Adam Borowski, Adam Osiowy + +**Podprojekt:** Adam Osiowy - *segregator śmieci* + +--- + +## Opis podprojektu: + +- w projekcie wykorzystane zostały drzewa decyzyjne jako metoda uczenia +- projekt podzielony jest na 4 pliki +- plik tworzenie_danych_AO.py jest odpowiedzialny za wydobycie z każdego zdjęcia własności i zapis ich do pliku +![4](resources/screenShots/adamo4.png) +- w pliku uczenie_adamO.py znajdują się funkcje odpowiedzialne za uczenie i testowanie modelu +![5](resources/screenShots/adamo5.png) +- plik parametry_zdjec.h5 zawiera własności wszystkich zdjęć wykorzystanych w projekcie +- plik etykiety.h5 zawiera odpowiedni typ każdego ze zdjęć (glass,paper,plastic,metal) + +--- + +## Ogólne działanie: + +- na początku zbierane są informacje o każdym zdjęciu +``` +momenty = wyznaczHuMomenty(zdj) +haralick = wyznaczHaralick(zdj) +histogram = wyznaczHistogram(zdj) +``` +- wybrane własności to: + 1. Histogram kolorów okreslający rozkład jasności pixeli w każdej komórce na zdjęciu w skali szarości + ![6](resources/screenShots/adamo6.png) + zdjęcie jest przekształcane do przestrzeni barw hsv + po czym wyliczany jest histogram podając do funkcji zdjęcie, kanały (hsv), maskę, podział zdjęcia na 512 przedziałów (8x8x8), zakres każdego kanału + 2. Momenty obrazu (Hu Moments) określające kształt obiektu na zdjęciu + ![8](resources/screenShots/adamo8.png) + są średnią ważoną intensywności pikseli obrazu. + Są liczone ze wzoru: + ![10](resources/screenShots/adamo10.png) + gdzie I(x,y) to intensywność pixela w danym punkcie + *Momenty surowe* - informują o intensywności pikseli i ich położeniu na obrazie + *Momenty centralne* - otrzymujemy po odjęciu od momentów surowych środka ciężkości danego kształtu + ![11](resources/screenShots/adamo11.png) + momenty te są niezmienne w wyliczaniu to znaczy że jeśli kształt jest ten sam to nie ważne jest jego położenie na zdjęciu + *Momenty Hu* - to zbiór 7 liczb obliczonych na podstawie momentów centralnych. + Pierwsze 6 momentów są niezmienne dla translacji, skali i rotacji. + Podczas gdy znak siódmej liczby zmienia się wraz z odbiciem kształu (względem osi). + ![12](resources/screenShots/adamo12.png) + + 3. Tekstura Haralicka określająca nasycenie ilości pixeli w skali szarości + ![7](resources/screenShots/adamo7.png) + Haralick zasugerował zastosowanie macierzy współwystępowania poziomu szarości (GLCM). + Ta metoda opiera się na połączonych rozkładach prawdopodobieństwa par pikseli. + GLCM pokazuje, jak często każdy poziom szarości występuje w pikselach umieszczonych w ustalonym położeniu + geometrycznym względem siebie, w zależności od poziomu szarości + ![13](resources/screenShots/adamo13.png) + +- własności sa zapisywane jako macierze, ustawiane w szereg jako wiersz i zapisywane do pliku z danymi .h5 +``` +wiersz = np.hstack([momenty, histogram, haralick]) +``` +- dane dzielone są losowo na 2 pary, jedna testowa druga treningowa +``` +(uczenieDane, testowanieDane, uczenieEtykiety, testowanieEtykiety) = +train_test_split(np.array(dane), np.array(etykiety), test_size=rozmiar_zbioru_testowego) +``` +gdzie rozmiar zbioru testowego określony wcześniej na 20% +- tworzony jest estymator +``` +rfc = RandomForestClassifier(max_depth=15, n_jobs=4, random_state=1) +``` +gdzie n_jobs to ilość wątków, random_state pilnuje aby zbiór był zawsze dzielony tak samo, +a max_depth to maksymalna głebokość każdego drzewa +estymator domyślnie korzysta ze strategii opierającej się o indeks Giniego +``` +'indeks Giniego jest to miara która określa jak często losowo wybrany element zostanie błędnie zidentyfikowany' +``` +to znaczy ze element z najmniejszym indeksem Giniego powinien zostać wybrany + +indeks jest obliczany ze wzoru: + +![9](resources/screenShots/adamo9.png) + +[przykład](https://www.geeksforgeeks.org/decision-tree-introduction-example/) +- estymator rozpoczyna uczenie korzystając ze zbiorów treningowych +``` +rfc.fit(uczenieDane, uczenieEtykiety) +``` +- następnie wyliczana jest skuteczność na zbiorach testowych +``` +rfc.score(testowanieDane, testowanieEtykiety) +``` + +--- + +## Integracja z projektem zespołowym: + +- Przy starcie programu estymator rozpoczyna nauke +``` +rfc = adamO.rozpocznijUczenie() +``` +- Śmieciarka porusza się po domach zbierając z nich śmieci +- Po zebraniu wszystkich śmieci kieruje się na wysypisko +- Każde zdjęcie śmieci jest segregowane z wykorzystaniem funkcji przewidującej typ +``` +rodzaj = adamO.przewidz(smiec, rfc) +``` +![3](resources/screenShots/adamo3.png) +- Zdjęcia posegregowanych śmieci umieszczane są w odpowiednich folderach: + +![1](resources/screenShots/adamo1.png) +- Na koniec wyświetlane są losowo wybrane zdjęcia śmieci z kontenerów wraz z informacją o typie ustalonym przez estymator +![2](resources/screenShots/adamo2.png) + 1. górny napis to typ zwrócony przez estymator + 2. drugi napis to wartości prawpopodobieństwa z jakim estymator ocenił typ + 3. trzeci napis to nazwa zdjęcia + +--- + +## Efekt działania programu w postaci drzewa decyzyjnego: +![10](graph.png) diff --git a/graph.png b/graph.png new file mode 100644 index 0000000000000000000000000000000000000000..422517ba68015b234f081d216941ddf055b3c1d3 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