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| """ | |
| binarizer.py | |
| ------------ | |
| Converts a grayscale floor plan image into a clean binary (black/white) image. | |
| Pipeline: | |
| 1. Gaussian blur → reduce sensor/scan noise | |
| 2. Adaptive threshold → handle uneven lighting across the page | |
| 3. Morphological close → fill tiny gaps in wall lines | |
| 4. Morphological open → remove isolated specks | |
| """ | |
| import cv2 | |
| import numpy as np | |
| def binarize( | |
| img: np.ndarray, | |
| blur_kernel: int = 5, | |
| block_size: int = 25, | |
| c_offset: int = 10, | |
| morph_kernel: int = 3, | |
| ) -> np.ndarray: | |
| """ | |
| Convert a grayscale image to a clean binary image. | |
| Args: | |
| img: Grayscale uint8 numpy array. | |
| blur_kernel: Gaussian blur kernel size (must be odd). | |
| block_size: Neighbourhood size for adaptive threshold (must be odd, ≥3). | |
| c_offset: Constant subtracted from the mean in adaptive threshold. | |
| Higher = more aggressive (removes faint lines too). | |
| morph_kernel: Kernel size for morphological cleanup. | |
| Returns: | |
| Binary image (0 = background, 255 = foreground/walls), uint8. | |
| """ | |
| _validate_grayscale(img) | |
| # 1. Gaussian blur to suppress scan noise | |
| blurred = cv2.GaussianBlur(img, (blur_kernel, blur_kernel), 0) | |
| # 2. Adaptive threshold — handles uneven illumination better than Otsu | |
| # THRESH_BINARY_INV: walls/lines become white (255) on black background | |
| binary = cv2.adaptiveThreshold( | |
| blurred, | |
| maxValue=255, | |
| adaptiveMethod=cv2.ADAPTIVE_THRESH_GAUSSIAN_C, | |
| thresholdType=cv2.THRESH_BINARY_INV, | |
| blockSize=block_size, | |
| C=c_offset, | |
| ) | |
| # 3. Morphological closing: fills small breaks in wall lines | |
| close_kernel = cv2.getStructuringElement( | |
| cv2.MORPH_RECT, (morph_kernel, morph_kernel) | |
| ) | |
| binary = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, close_kernel) | |
| # 4. Morphological opening: removes isolated noise specks | |
| open_kernel = cv2.getStructuringElement( | |
| cv2.MORPH_RECT, (morph_kernel, morph_kernel) | |
| ) | |
| binary = cv2.morphologyEx(binary, cv2.MORPH_OPEN, open_kernel) | |
| return binary | |
| def remove_small_components( | |
| binary: np.ndarray, min_area: int = 100 | |
| ) -> np.ndarray: | |
| """ | |
| Remove connected components smaller than min_area pixels. | |
| Useful for eliminating text fragments and scan artifacts. | |
| Args: | |
| binary: Binary image (uint8, values 0 or 255). | |
| min_area: Components with fewer pixels than this are removed. | |
| Returns: | |
| Cleaned binary image. | |
| """ | |
| _validate_grayscale(binary) | |
| num_labels, labels, stats, _ = cv2.connectedComponentsWithStats( | |
| binary, connectivity=8 | |
| ) | |
| # Background is label 0 — skip it | |
| cleaned = np.zeros_like(binary) | |
| for label in range(1, num_labels): | |
| area = stats[label, cv2.CC_STAT_AREA] | |
| if area >= min_area: | |
| cleaned[labels == label] = 255 | |
| return cleaned | |
| def enhance_contrast(img: np.ndarray) -> np.ndarray: | |
| """ | |
| Apply CLAHE (Contrast Limited Adaptive Histogram Equalization). | |
| Improves visibility of faint lines before thresholding. | |
| Args: | |
| img: Grayscale uint8 numpy array. | |
| Returns: | |
| Contrast-enhanced grayscale image. | |
| """ | |
| _validate_grayscale(img) | |
| clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) | |
| return clahe.apply(img) | |
| def _validate_grayscale(img: np.ndarray) -> None: | |
| if img is None or not isinstance(img, np.ndarray): | |
| raise TypeError("Input must be a numpy ndarray.") | |
| if len(img.shape) != 2: | |
| raise ValueError( | |
| f"Expected a grayscale (2D) image, got shape {img.shape}. " | |
| "Convert to grayscale first." | |
| ) | |