| 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 |