File size: 3,959 Bytes
a5c15eb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 | import cv2
import numpy as np
import logging
logger = logging.getLogger(__name__)
class EnhancedPicture:
def __init__(self, debug=False):
if debug:
logging.basicConfig(level=logging.DEBUG)
else:
logging.basicConfig(level=logging.WARNING)
def _load_image(self, image_input):
if isinstance(image_input, str):
image = cv2.imread(image_input)
if image is None:
raise FileNotFoundError(f"Gambar tidak ditemukan: {image_input}")
elif isinstance(image_input, np.ndarray):
image = image_input.copy()
else:
raise ValueError("Input harus berupa path (str) atau numpy array")
return image
def _analyze(self, image):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
brightness = np.mean(gray)
noise = np.std(gray)
logger.debug(f"Analisa — Brightness:{brightness:.1f} Noise:{noise:.1f}")
return brightness, noise
def correct_lighting(self, image):
lab = cv2.cvtColor(image, cv2.COLOR_BGR2LAB)
l, a, b = cv2.split(lab)
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
l_clahe = clahe.apply(l)
brightness = np.mean(l_clahe)
if brightness < 80:
gamma = 0.6
elif brightness > 180:
gamma = 1.6
else:
gamma = 1.0
if gamma != 1.0:
inv_gamma = 1.0 / gamma
table = np.array([
((i / 255.0) ** inv_gamma) * 255 for i in range(256)
]).astype("uint8")
l_clahe = cv2.LUT(l_clahe, table)
return cv2.cvtColor(cv2.merge([l_clahe, a, b]), cv2.COLOR_LAB2BGR)
def reduce_noise(self, image):
return cv2.fastNlMeansDenoisingColored(
image, None, h=10, hColor=10,
templateWindowSize=7, searchWindowSize=21
)
def normalize_size(self, image):
h, w = image.shape[:2]
if w < 640:
ratio = 640 / w
image = cv2.resize(image, (640, int(h * ratio)), interpolation=cv2.INTER_CUBIC)
elif w > 1280:
ratio = 1280 / w
image = cv2.resize(image, (1280, int(h * ratio)), interpolation=cv2.INTER_AREA)
return image
def process(self, image_input):
"""Aktifkan preprocessing"""
image = self._load_image(image_input)
brightness, noise = self._analyze(image)
if brightness < 80 or brightness > 180:
image = self.correct_lighting(image)
logger.debug(f"[1] Lighting diterapkan (brightness={brightness:.1f})")
else:
logger.debug(f"[1] Lighting dilewati (normal={brightness:.1f})")
if noise > 60:
image = self.reduce_noise(image)
logger.debug(f"[2] Denoise diterapkan (noise={noise:.1f})")
else:
logger.debug(f"[2] Denoise dilewati (noise={noise:.1f})")
image = self.normalize_size(image)
logger.debug(f"[3] Output: {image.shape}")
return image
def process_without_resize(self, image_input):
image = self._load_image(image_input)
brightness, noise = self._analyze(image)
if brightness < 80 or brightness > 180:
image = self.correct_lighting(image)
logger.debug(f"[1] Lighting diterapkan (brightness={brightness:.1f})")
else:
logger.debug(f"[1] Lighting dilewati (normal={brightness:.1f})")
if noise > 60:
image = self.reduce_noise(image)
logger.debug(f"[2] Denoise diterapkan (noise={noise:.1f})")
else:
logger.debug(f"[2] Denoise dilewati (noise={noise:.1f})")
logger.debug(f"[3] Output (no-resize): {image.shape}")
return image |