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import numpy as np
from typing import Union
class Preprocessor:
"""Stage 0: Image preprocessing for BOM engineering drawings."""
def load_image(self, path: str) -> np.ndarray:
"""Load image from path and return as grayscale uint8 array.
Args:
path: File path to image (PNG, JPG, TIFF supported).
Returns:
Grayscale numpy array (uint8, 0-255).
Raises:
ValueError: If image cannot be read.
"""
img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
if img is None:
raise ValueError(f"Cannot read image: {path}")
return img
def binarize(self, img: np.ndarray, method: str = "adaptive") -> np.ndarray:
"""Binarize grayscale image.
Args:
img: Grayscale numpy array.
method: "adaptive" | "otsu" | "none"
Returns:
Binary image with pixel values 0 or 255.
"""
if method == "adaptive":
return cv2.adaptiveThreshold(
img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY, blockSize=15, C=4
)
elif method == "otsu":
_, binary = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
return binary
elif method == "none":
if len(img.shape) == 3:
return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
return img
else:
raise ValueError(f"Unknown binarize method: {method}. Use 'adaptive', 'otsu', or 'none'.")
def denoise(self, img: np.ndarray, kernel_size: int = 2) -> np.ndarray:
"""Apply morphological closing to fill broken thin lines.
Args:
img: Binary or grayscale image.
kernel_size: Size of the morphological kernel (keep small to preserve thin lines).
Returns:
Denoised image.
"""
kernel = cv2.getStructuringElement(
cv2.MORPH_ELLIPSE, (kernel_size, kernel_size)
)
return cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)
def resize_if_needed(self, img: np.ndarray, max_dim: int = 4096) -> np.ndarray:
"""Resize image if its largest dimension exceeds max_dim, preserving aspect ratio.
Args:
img: Input image.
max_dim: Maximum allowed dimension in pixels.
Returns:
Resized image (or original if already within bounds).
"""
h, w = img.shape[:2]
largest = max(h, w)
if largest <= max_dim:
return img
scale = max_dim / largest
new_w = int(w * scale)
new_h = int(h * scale)
return cv2.resize(img, (new_w, new_h), interpolation=cv2.INTER_AREA)
def suppress_text_noise(self, img: np.ndarray, max_area: int = 180) -> np.ndarray:
"""Remove small isolated strokes (text labels) from a binary image.
Uses connected-component analysis to erase blobs that are small and
elongated — the typical signature of text characters — while preserving
larger, rounder component symbols.
Args:
img: Binary image (white background, black strokes).
max_area: Components with pixel area below this threshold AND
aspect ratio > 1.3 are treated as text and erased.
Returns:
Binary image with text-like blobs replaced by white.
"""
inv = cv2.bitwise_not(img)
n_labels, labels, stats, _ = cv2.connectedComponentsWithStats(inv, connectivity=8)
result = img.copy()
for lbl in range(1, n_labels):
area = int(stats[lbl, cv2.CC_STAT_AREA])
w = int(stats[lbl, cv2.CC_STAT_WIDTH])
h = int(stats[lbl, cv2.CC_STAT_HEIGHT])
if w == 0 or h == 0:
continue
aspect = max(w, h) / min(w, h)
if area < max_area and aspect > 1.3:
result[labels == lbl] = 255
return result
def dilate_strokes(self, img: np.ndarray, kernel_size: int = 5) -> np.ndarray:
"""Thicken black strokes in a binary image by eroding (strokes are black on white bg).
Useful when template uses thin stylized lines but drawing uses bold strokes.
Args:
img: Binary image (white background, black strokes).
kernel_size: Size of dilation kernel.
Returns:
Image with thickened strokes.
"""
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (kernel_size, kernel_size))
return cv2.erode(img, kernel)
def clahe_enhance(
self, img: np.ndarray, clip_limit: float = 2.0, tile_size: int = 8
) -> np.ndarray:
"""Apply CLAHE (Contrast Limited Adaptive Histogram Equalization).
Improves local contrast in drawings with uneven line density — faint
strokes that are washed out globally become visible after CLAHE.
Applied to grayscale images BEFORE binarization.
Args:
img: Grayscale uint8 image.
clip_limit: Threshold for contrast limiting (higher = stronger).
tile_size: Grid size in pixels for local histogram computation.
Returns:
Contrast-enhanced grayscale image.
"""
clahe = cv2.createCLAHE(
clipLimit=clip_limit, tileGridSize=(tile_size, tile_size)
)
return clahe.apply(img.astype(np.uint8))
def normalize_strokes(
self, img: np.ndarray, target_width: int = 2
) -> np.ndarray:
"""Normalize stroke width via thinning + uniform dilation.
Engineering drawings may scan at different DPIs, producing thick or
thin lines. This normalizes all strokes to `target_width` pixels:
1. Morphological thinning (iterative erosion to approximate skeleton)
2. Dilation to the desired target width
This makes NCC matching invariant to line-width variation between the
template and the drawing, which is a common source of false negatives.
Args:
img: Binary image (white background, black strokes).
target_width: Desired uniform stroke width in pixels.
Returns:
Binary image with normalized stroke width.
"""
inv = cv2.bitwise_not(img)
# Iterative thinning: erode until no more pixels can be removed.
# Stop after 20 iterations to bound runtime.
kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (3, 3))
prev = np.zeros_like(inv)
for _ in range(20):
eroded = cv2.erode(inv, kernel)
temp = cv2.dilate(eroded, kernel)
diff = cv2.subtract(inv, temp)
skeleton = cv2.bitwise_or(prev, diff)
inv = eroded.copy()
prev = skeleton.copy()
if cv2.countNonZero(inv) == 0:
break
# Re-dilate skeleton to target width
if target_width > 1:
kw = target_width * 2 - 1
dk = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (kw, kw))
skeleton = cv2.dilate(skeleton, dk)
return cv2.bitwise_not(skeleton)
def preprocess(
self,
img_or_path: Union[str, np.ndarray],
binarize_method: str = "adaptive",
denoise: bool = True,
dilate_strokes: int = 0,
clahe: bool = False,
normalize_stroke_width: int = 0,
) -> dict:
"""Full preprocessing pipeline.
Args:
img_or_path: File path string or numpy array.
binarize_method: Binarization method passed to self.binarize().
denoise: Whether to apply morphological denoising.
Returns:
Dict with keys:
"original": original grayscale numpy array
"processed": preprocessed numpy array
"scale_factor": downscale ratio applied (1.0 if no resize)
"""
if isinstance(img_or_path, str):
original = self.load_image(img_or_path)
elif isinstance(img_or_path, np.ndarray):
if len(img_or_path.shape) == 3:
original = cv2.cvtColor(img_or_path, cv2.COLOR_RGB2GRAY)
else:
original = img_or_path.copy()
else:
raise ValueError("img_or_path must be a file path string or numpy array.")
resized = self.resize_if_needed(original)
h_orig, w_orig = original.shape[:2]
h_res, w_res = resized.shape[:2]
scale_factor = h_res / h_orig if h_orig > 0 else 1.0
# CLAHE before binarization: improves faint-stroke visibility
to_binarize = self.clahe_enhance(resized) if clahe else resized
processed = self.binarize(to_binarize, method=binarize_method)
if denoise:
processed = self.denoise(processed)
if dilate_strokes > 0:
processed = self.dilate_strokes(processed, kernel_size=dilate_strokes)
if normalize_stroke_width > 0:
processed = self.normalize_strokes(processed, target_width=normalize_stroke_width)
return {
"original": original,
"processed": processed,
"scale_factor": scale_factor,
}
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