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Ekstraksi fitur lokal SIFT & SURF, serta momen HSV, dibatasi pada area ROI daging (mask).
Membutuhkan opencv-contrib-python versi yang dikompilasi dengan
OPENCV_ENABLE_NONFREE=ON (mis. 3.4.2.16) agar cv2.xfeatures2d.SIFT_create()
dan cv2.xfeatures2d.SURF_create() tersedia.
"""
import time
import cv2
import numpy as np
from scipy.stats import skew
import config
def _get_detector(method):
method = method.lower()
if method == "sift":
return cv2.xfeatures2d.SIFT_create(nfeatures=config.SIFT_N_FEATURES)
elif method == "surf":
return cv2.xfeatures2d.SURF_create(
hessianThreshold=config.SURF_HESSIAN_THRESHOLD
)
raise ValueError("method harus 'sift' atau 'surf', diterima: {}".format(method))
def calculate_hsv_moments(hsv_image, mask):
"""
Mengekstrak momen statistik (Mean, Std, Skewness) dari citra HSV.
Hanya menghitung piksel yang berada di dalam mask (daging).
"""
h, s, v = cv2.split(hsv_image)
moments = []
for channel in (h, s, v):
# Terapkan mask untuk mengisolasi area daging secara eksklusif
channel_data = channel[mask == 255]
if len(channel_data) == 0:
moments.extend([0.0, 0.0, 0.0])
continue
moments.extend([
float(np.mean(channel_data)),
float(np.std(channel_data)),
float(skew(channel_data))
])
return np.array(moments, dtype=np.float32)
def extract_features(gray_image, hsv_image, mask, method):
"""
Mendeteksi keypoint & menghitung descriptor pada area mask saja,
serta mengekstrak momen warna global HSV.
Returns
-------
descriptors : np.ndarray (N, D) float32
hsv_moments : np.ndarray (9,) float32
n_keypoints : int
elapsed_sec : float
"""
detector = _get_detector(method)
start = time.perf_counter()
keypoints, descriptors = detector.detectAndCompute(gray_image, mask)
# Ekstraksi HSV Moments sesuai parameter konfigurasi
hsv_moments = calculate_hsv_moments(hsv_image, mask) if config.USE_COLOR_FUSION else None
elapsed_sec = time.perf_counter() - start
n_keypoints = len(keypoints) if keypoints is not None else 0
if descriptors is None:
descriptors = np.empty((0, 128 if method.lower() == "sift" else 64),
dtype=np.float32)
else:
descriptors = descriptors.astype(np.float32)
cap = config.MAX_DESCRIPTORS_PER_IMAGE
if cap is not None and len(descriptors) > cap:
rng = np.random.RandomState(config.RANDOM_STATE)
idx = rng.choice(len(descriptors), size=cap, replace=False)
descriptors = descriptors[idx]
return descriptors, hsv_moments, n_keypoints, elapsed_sec |