CommonZhuan Preprocessing Scripts 1.0
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CrbnsCat10n
299
PreProcessing_Common/scripts/clean_characters.py
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299
PreProcessing_Common/scripts/clean_characters.py
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from __future__ import annotations
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import argparse
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from pathlib import Path
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import cv2
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import numpy as np
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IMAGE_SUFFIXES = {".png", ".jpg", ".jpeg", ".tif", ".tiff", ".bmp", ".webp"}
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def read_image(path: Path) -> np.ndarray:
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raw = np.fromfile(str(path), dtype=np.uint8)
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image = cv2.imdecode(raw, cv2.IMREAD_COLOR)
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if image is None:
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raise ValueError(f"Cannot read image: {path}")
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return image
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def write_image(path: Path, image: np.ndarray) -> None:
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ok, encoded = cv2.imencode(path.suffix, image)
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if not ok:
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raise ValueError(f"Cannot encode image: {path}")
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encoded.tofile(str(path))
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def binarize_foreground(gray: np.ndarray) -> np.ndarray:
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threshold, _ = cv2.threshold(
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gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU
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)
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gray_min = int(gray.min())
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gray_max = int(gray.max())
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if threshold <= gray_min or threshold >= gray_max:
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threshold = (gray_min + gray_max) / 2.0
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border = np.concatenate([gray[0, :], gray[-1, :], gray[:, 0], gray[:, -1]])
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background_is_light = np.median(border) >= 128
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if background_is_light:
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return (gray < threshold).astype(np.uint8) * 255
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return (gray > threshold).astype(np.uint8) * 255
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def foreground_bbox(mask: np.ndarray) -> tuple[int, int, int, int]:
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ys, xs = np.where(mask > 0)
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if xs.size == 0:
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height, width = mask.shape
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return 0, 0, width, height
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return int(xs.min()), int(ys.min()), int(xs.max() + 1), int(ys.max() + 1)
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def remove_tiny_components(mask: np.ndarray, min_area: int) -> np.ndarray:
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if min_area <= 0:
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return mask
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num_labels, labels, stats, _ = cv2.connectedComponentsWithStats(
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mask, connectivity=8
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)
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cleaned = np.zeros_like(mask)
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for label in range(1, num_labels):
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area = int(stats[label, cv2.CC_STAT_AREA])
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if area >= min_area:
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cleaned[labels == label] = 255
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return cleaned
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def remove_isolated_specks(
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mask: np.ndarray,
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max_area: int,
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support_radius: int,
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) -> np.ndarray:
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if max_area <= 0 or support_radius <= 0:
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return mask
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num_labels, labels, stats, _ = cv2.connectedComponentsWithStats(
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mask, connectivity=8
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)
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support = np.zeros_like(mask)
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candidate_labels: list[int] = []
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for label in range(1, num_labels):
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area = int(stats[label, cv2.CC_STAT_AREA])
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if area <= max_area:
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candidate_labels.append(label)
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else:
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support[labels == label] = 255
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if not candidate_labels or not np.any(support):
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return mask
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kernel_size = support_radius * 2 + 1
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kernel = cv2.getStructuringElement(
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cv2.MORPH_ELLIPSE, (kernel_size, kernel_size)
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)
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nearby_support = cv2.dilate(support, kernel)
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cleaned = mask.copy()
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for label in candidate_labels:
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component = labels == label
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if not np.any(nearby_support[component]):
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cleaned[component] = 0
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return cleaned
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def remove_border_hairlines(mask: np.ndarray, max_thickness: int = 8) -> np.ndarray:
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num_labels, labels, stats, _ = cv2.connectedComponentsWithStats(
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mask, connectivity=8
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)
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height, width = mask.shape
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cleaned = mask.copy()
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border_margin = max(4, int(round(min(width, height) * 0.02)))
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for label in range(1, num_labels):
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x = int(stats[label, cv2.CC_STAT_LEFT])
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y = int(stats[label, cv2.CC_STAT_TOP])
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comp_width = int(stats[label, cv2.CC_STAT_WIDTH])
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comp_height = int(stats[label, cv2.CC_STAT_HEIGHT])
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near_border = (
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x <= border_margin
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or y <= border_margin
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or x + comp_width >= width - border_margin
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or y + comp_height >= height - border_margin
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)
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vertical_hairline = comp_width <= max_thickness and comp_height >= 24
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horizontal_hairline = comp_height <= max_thickness and comp_width >= 24
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if near_border and (vertical_hairline or horizontal_hairline):
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cleaned[labels == label] = 0
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return cleaned
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def fill_small_holes(mask: np.ndarray, max_area: int) -> np.ndarray:
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if max_area <= 0:
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return mask
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foreground = mask > 0
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inverse = (~foreground).astype(np.uint8)
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flood_mask = np.zeros((mask.shape[0] + 2, mask.shape[1] + 2), dtype=np.uint8)
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cv2.floodFill(inverse, flood_mask, (0, 0), 2)
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holes = (inverse == 1).astype(np.uint8)
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num_labels, labels, stats, _ = cv2.connectedComponentsWithStats(
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holes, connectivity=8
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)
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filled = foreground.copy()
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for label in range(1, num_labels):
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area = int(stats[label, cv2.CC_STAT_AREA])
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width = int(stats[label, cv2.CC_STAT_WIDTH])
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height = int(stats[label, cv2.CC_STAT_HEIGHT])
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if area <= max_area and max(width, height) <= max(3, int(max_area**0.5) * 4):
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filled[labels == label] = True
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return filled.astype(np.uint8) * 255
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def smooth_edges_once(mask: np.ndarray) -> np.ndarray:
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foreground = mask > 0
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kernel = np.ones((3, 3), dtype=np.uint8)
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neighbors = cv2.filter2D(
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foreground.astype(np.uint8),
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ddepth=-1,
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kernel=kernel,
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borderType=cv2.BORDER_CONSTANT,
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)
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smoothed = (foreground & (neighbors >= 3)) | (~foreground & (neighbors >= 7))
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return smoothed.astype(np.uint8) * 255
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def clean_mask(
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mask: np.ndarray,
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min_component_area: int,
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max_hole_area: int,
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max_isolated_speck_area: int,
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smooth: bool = True,
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) -> np.ndarray:
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x1, y1, x2, y2 = foreground_bbox(mask)
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support_radius = max(12, min(42, int(round(min(x2 - x1, y2 - y1) * 0.035))))
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cleaned = remove_tiny_components(mask, min_component_area)
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cleaned = remove_isolated_specks(
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cleaned,
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max_area=max_isolated_speck_area,
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support_radius=support_radius,
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)
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cleaned = remove_border_hairlines(cleaned)
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cleaned = fill_small_holes(cleaned, max_hole_area)
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if smooth:
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cleaned = smooth_edges_once(cleaned)
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cleaned = remove_isolated_specks(
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cleaned,
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max_area=max_isolated_speck_area,
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support_radius=support_radius,
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)
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cleaned = remove_border_hairlines(cleaned)
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cleaned = fill_small_holes(cleaned, max_hole_area)
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return remove_tiny_components(cleaned, min_component_area)
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def adaptive_clean_params(mask: np.ndarray) -> tuple[int, int, int]:
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x1, y1, x2, y2 = foreground_bbox(mask)
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bbox_area = max(1, (x2 - x1) * (y2 - y1))
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min_component_area = max(4, min(24, int(round(bbox_area * 0.000018))))
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max_hole_area = max(12, min(90, int(round(bbox_area * 0.00008))))
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max_isolated_speck_area = max(
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min_component_area + 4,
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min(48, int(round(bbox_area * 0.000055))),
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)
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return min_component_area, max_hole_area, max_isolated_speck_area
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def mask_to_display(mask: np.ndarray) -> np.ndarray:
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return np.where(mask > 0, 0, 255).astype(np.uint8)
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def process_image(
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image_path: Path,
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output_dir: Path,
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min_component_area: int | None,
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max_hole_area: int | None,
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max_isolated_speck_area: int | None,
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smooth: bool,
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) -> None:
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original = read_image(image_path)
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gray = cv2.cvtColor(original, cv2.COLOR_BGR2GRAY)
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binary = binarize_foreground(gray)
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(
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adaptive_min_area,
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adaptive_max_hole_area,
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adaptive_max_isolated_speck_area,
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) = adaptive_clean_params(binary)
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cleaned = clean_mask(
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binary,
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min_component_area=(
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adaptive_min_area if min_component_area is None else min_component_area
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),
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max_hole_area=(
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adaptive_max_hole_area if max_hole_area is None else max_hole_area
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),
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max_isolated_speck_area=(
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adaptive_max_isolated_speck_area
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if max_isolated_speck_area is None
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else max_isolated_speck_area
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),
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smooth=smooth,
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)
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write_image(output_dir / f"{image_path.stem}_cleaned.png", mask_to_display(cleaned))
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def iter_images(input_dir: Path) -> list[Path]:
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return sorted(
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path
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for path in input_dir.iterdir()
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if path.is_file() and path.suffix.lower() in IMAGE_SUFFIXES
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)
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def build_parser() -> argparse.ArgumentParser:
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parser = argparse.ArgumentParser(
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description="Gently clean complex seal-script characters."
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)
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parser.add_argument(
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"--input-dir",
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type=Path,
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default=Path("PreProcessing_Common/original_characters"),
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)
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parser.add_argument(
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"--output-dir",
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type=Path,
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default=Path("PreProcessing_Common/cleaned_characters"),
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)
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parser.add_argument("--min-component-area", type=int, default=None)
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parser.add_argument("--max-hole-area", type=int, default=None)
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parser.add_argument("--max-isolated-speck-area", type=int, default=None)
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parser.add_argument("--no-smooth", action="store_true")
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return parser
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def main() -> None:
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args = build_parser().parse_args()
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args.output_dir.mkdir(parents=True, exist_ok=True)
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image_paths = iter_images(args.input_dir)
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if not image_paths:
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raise SystemExit(f"No images found in {args.input_dir}")
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for image_path in image_paths:
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process_image(
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image_path=image_path,
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output_dir=args.output_dir,
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min_component_area=args.min_component_area,
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max_hole_area=args.max_hole_area,
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max_isolated_speck_area=args.max_isolated_speck_area,
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smooth=not args.no_smooth,
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)
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print(f"processed: {image_path.name}")
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if __name__ == "__main__":
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main()
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