71 lines
1.7 KiB
Python
71 lines
1.7 KiB
Python
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from sklearn.preprocessing import LabelEncoder
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import numpy as np
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import mahotas
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import cv2
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import os
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import h5py
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rozmiar_zdj = tuple((500, 500))
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sciezka_do_zdj = "resources/smieci_stare"
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h5_dane = 'parametry_zdjec.h5'
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h5_etykiety = 'etykiety.h5'
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def hu_moments(zdj):
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zdj = cv2.cvtColor(zdj, cv2.COLOR_BGR2GRAY)
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momenty = cv2.HuMoments(cv2.moments(zdj)).flatten()
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return momenty
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def haralick(zdj):
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szare_zdj = cv2.cvtColor(zdj, cv2.COLOR_BGR2GRAY)
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haralick = mahotas.features.haralick(szare_zdj).mean(axis=0)
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return haralick
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def histogram(zdj, mask=None):
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zdj = cv2.cvtColor(zdj, cv2.COLOR_BGR2HSV)
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hist = cv2.calcHist([zdj], [0, 1, 2], mask, [8, 8, 8], [0, 256, 0, 256, 0, 256])
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cv2.normalize(hist, hist)
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return hist.flatten()
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klasy = os.listdir(sciezka_do_zdj)
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klasy.sort()
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dane = []
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wszystkie_typy_zdj = []
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for klasa in klasy:
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katalog = os.path.join(sciezka_do_zdj, klasa)
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biezaca_klasa = klasa
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for plik in os.listdir(katalog):
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zdj = cv2.imread(os.path.join(katalog, plik))
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zdj = cv2.resize(zdj, rozmiar_zdj)
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fv_hu_moments = hu_moments(zdj)
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fv_haralick = haralick(zdj)
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fv_histogram = histogram(zdj)
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wiersz = np.hstack([fv_hu_moments, fv_histogram, fv_haralick])
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wszystkie_typy_zdj.append(biezaca_klasa)
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dane.append(wiersz)
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print(len(wszystkie_typy_zdj))
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targetNames = np.unique(wszystkie_typy_zdj)
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print(targetNames,targetNames.shape)
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le = LabelEncoder()
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target = le.fit_transform(wszystkie_typy_zdj)
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print(target.shape)
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h5f_data = h5py.File(h5_dane, 'w')
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h5f_data.create_dataset('dataset_1', data=np.array(dane))
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h5f_label = h5py.File(h5_etykiety, 'w')
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h5f_label.create_dataset('dataset_1', data=np.array(target))
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h5f_data.close()
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h5f_label.close()
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