108 lines
4.1 KiB
Python
108 lines
4.1 KiB
Python
import numpy as np
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import cv2
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from skimage.feature import graycomatrix, graycoprops
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import pandas as pd
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class FeatureExtractor:
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def __init__(self, n_hist_bins=8):
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self.n_hist_bins = n_hist_bins
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self.feature_names = [
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# RGB average (3)
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'avg_red', 'avg_green', 'avg_blue',
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# HSV mean (3)
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'mean_hue', 'mean_saturation', 'mean_value',
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# HSV std (3)
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'std_hue', 'std_saturation', 'std_value',
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# GLCM (4)
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'contrast', 'homogeneity', 'correlation', 'energy',
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# Color histogram (3 channels x n_hist_bins)
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*[f'hist_r_{i}' for i in range(n_hist_bins)],
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*[f'hist_g_{i}' for i in range(n_hist_bins)],
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*[f'hist_b_{i}' for i in range(n_hist_bins)],
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# Hu moments (7)
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*[f'hu_moment_{i+1}' for i in range(7)],
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]
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def extract_rgb_average(self, image_rgb):
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if image_rgb is None:
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return [0.0, 0.0, 0.0]
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try:
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return [np.mean(image_rgb[:, :, c]) for c in range(3)]
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except Exception:
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return [0.0, 0.0, 0.0]
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def extract_hsv_features(self, image_rgb):
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if image_rgb is None:
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return [0.0] * 6
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try:
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hsv = cv2.cvtColor(image_rgb, cv2.COLOR_RGB2HSV).astype(np.float32)
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hsv[:, :, 0] *= 360.0 / 180.0 # hue 0-360
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means = [np.mean(hsv[:, :, c]) for c in range(3)]
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stds = [np.std(hsv[:, :, c]) for c in range(3)]
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return means + stds
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except Exception:
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return [0.0] * 6
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def glcm_features(self, gray_image):
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if gray_image is None:
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return [0.0] * 6
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try:
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gray = gray_image.astype(np.uint8)
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glcm = graycomatrix(gray, distances=[1, 2, 3],
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angles=[0, np.pi / 4, np.pi / 2, 3 * np.pi / 4],
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levels=256, symmetric=True, normed=True)
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props = ['contrast', 'homogeneity', 'correlation', 'energy']
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return [np.mean(graycoprops(glcm, prop)) for prop in props]
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except Exception:
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return [0.0] * 6
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def extract_color_histogram(self, image_rgb):
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if image_rgb is None:
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return [0.0] * (3 * self.n_hist_bins)
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try:
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features = []
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for c in range(3):
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hist = cv2.calcHist([image_rgb], [c], None,
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[self.n_hist_bins], [0, 256])
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hist = hist.flatten() / (image_rgb.shape[0] * image_rgb.shape[1])
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features.extend(hist.tolist())
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return features
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except Exception:
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return [0.0] * (3 * self.n_hist_bins)
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def extract_hu_moments(self, gray_processed):
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if gray_processed is None:
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return [0.0] * 7
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try:
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binary = (gray_processed > 0).astype(np.uint8) * 255
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moments = cv2.moments(binary)
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hu = cv2.HuMoments(moments)
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# Log-scale Hu moments for numerical stability
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hu = [-np.sign(h) * np.log10(np.abs(h) + 1e-10) for h in hu.flatten()]
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return hu
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except Exception:
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return [0.0] * 7
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def extract_all_features(self, image_rgb, gray_processed):
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if image_rgb is None:
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print("[ERROR] image_rgb is None in extract_all_features")
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return [0.0] * len(self.feature_names)
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if gray_processed is None:
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print("[ERROR] gray_processed is None in extract_all_features")
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return [0.0] * len(self.feature_names)
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features = []
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features.extend(self.extract_rgb_average(image_rgb))
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features.extend(self.extract_hsv_features(image_rgb))
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features.extend(self.glcm_features(gray_processed))
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features.extend(self.extract_color_histogram(image_rgb))
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features.extend(self.extract_hu_moments(gray_processed))
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return features
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def save_features_to_csv(self, features_list, labels, filename):
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df = pd.DataFrame(features_list, columns=self.feature_names)
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df['label'] = labels
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df.to_csv(filename, index=False)
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print(f"Fitur disimpan ke: {filename}")
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return df |