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new file: PreProcessing/.gitignore

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new file:   PreProcessing/README.md
	new file:   PreProcessing/scripts/compare_manual_matrix17.py
	new file:   PreProcessing/scripts/draw_matrix17.py
	modified:   PreProcessing/scripts/process_characters.py
	new file:   PreProcessing/scripts/run_preprocessing.py
This commit is contained in:
CrbnsCat10n
2026-05-13 14:28:34 +08:00
parent 60dbc51403
commit f2966f123d
10 changed files with 1890 additions and 168 deletions
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# Generated preprocessing outputs
comparisons/
manual_diffs/
manual_matrices/
original_characters/
processed_characters/
svg_characters/
out_cleaned/
out_matrix/
# Local caches
.matplotlib-cache/
scripts/__pycache__/
*.pyc

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# PreProcessing 预处理
这个目录包含17*17型特殊种类篆文字图像的预处理流程。日常批量处理请使用 `scripts/run_preprocessing.py`。
## 脚本用法
依赖见 `scripts/requirements.txt`。如果环境里还没有安装，可以先运行：
```bash
.venv/bin/pip install -r PreProcessing/scripts/requirements.txt
```
从项目根目录运行：
```bash
.venv/bin/python PreProcessing/scripts/run_preprocessing.py \
--input-dir PreProcessing/original_characters \
--cleaned-dir PreProcessing/out_cleaned \
--matrix-dir PreProcessing/out_matrix
```
参数含义：
- `--input-dir`：原始图像输入文件夹
- `--cleaned-dir`：cleaned 二值图输出文件夹
- `--matrix-dir`：17x17 矩阵文本输出文件夹
输出命名会和输入文件名对齐：
- cleaned 图像：`原文件名_cleaned.png`
- 矩阵文本：`原文件名_matrix.txt`
矩阵文本是纯 17 行，每行 17 个字符，只包含 `0` 和 `1`。
## 可选调参
默认参数会输出 17x17 矩阵。必要时可以追加这些参数：
- `--grid-size`，默认 `17`
- `--cell-samples`，默认 `32`
- `--open-kernel-size`
- `--close-kernel-size`
- `--median-size`
- `--min-component-area`
- `--triangle-threshold`
## 目录说明
`comparisons/`、`manual_diffs/`、`manual_matrices/`、`processed_characters/`、`svg_characters/` 等测试和比较用目录已经在 `PreProcessing/.gitignore` 中忽略。

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from __future__ import annotations
import argparse
from dataclasses import dataclass
from pathlib import Path
import cv2
import numpy as np
GRID_SIZE = 17
@dataclass(frozen=True)
class MatrixMetrics:
stem: str
true_positive: int
false_positive: int
false_negative: int
manual_cells: int
generated_cells: int
manual_2x2: int
generated_2x2: int
@property
def precision(self) -> float:
denominator = self.true_positive + self.false_positive
return self.true_positive / denominator if denominator else 1.0
@property
def recall(self) -> float:
denominator = self.true_positive + self.false_negative
return self.true_positive / denominator if denominator else 1.0
@property
def f1(self) -> float:
denominator = self.precision + self.recall
return 2.0 * self.precision * self.recall / denominator if denominator else 0.0
def read_matrix(path: Path) -> np.ndarray:
rows: list[str] = []
for line in path.read_text(encoding="utf-8").splitlines():
stripped = line.strip()
if not stripped or stripped.startswith("#"):
continue
rows.append(stripped)
if len(rows) == GRID_SIZE:
break
if len(rows) != GRID_SIZE:
raise ValueError(f"{path} has {len(rows)} matrix rows, expected {GRID_SIZE}")
matrix = np.zeros((GRID_SIZE, GRID_SIZE), dtype=np.uint8)
for row_index, row_text in enumerate(rows):
if len(row_text) < GRID_SIZE:
raise ValueError(
f"{path} row {row_index + 1} has {len(row_text)} columns, "
f"expected {GRID_SIZE}"
)
for col_index, char in enumerate(row_text[:GRID_SIZE]):
matrix[row_index, col_index] = 1 if char == "1" else 0
return matrix
def count_2x2_blocks(matrix: np.ndarray) -> int:
total = 0
for row in range(GRID_SIZE - 1):
for col in range(GRID_SIZE - 1):
total += int(matrix[row : row + 2, col : col + 2].sum() == 4)
return total
def diff_image(manual: np.ndarray, generated: np.ndarray, scale: int) -> np.ndarray:
both = (manual == 1) & (generated == 1)
false_positive = (manual == 0) & (generated == 1)
false_negative = (manual == 1) & (generated == 0)
image = np.full((GRID_SIZE, GRID_SIZE, 3), 255, dtype=np.uint8)
image[both] = (0, 0, 0)
image[false_positive] = (40, 40, 220)
image[false_negative] = (220, 80, 40)
return cv2.resize(image, (GRID_SIZE * scale, GRID_SIZE * scale), interpolation=cv2.INTER_NEAREST)
def write_image(path: Path, image: np.ndarray) -> None:
ok, encoded = cv2.imencode(path.suffix, image)
if not ok:
raise ValueError(f"Cannot encode image: {path}")
encoded.tofile(str(path))
def metrics_for_pair(manual_path: Path, generated_path: Path) -> MatrixMetrics:
manual = read_matrix(manual_path)
generated = read_matrix(generated_path)
true_positive = int(((manual == 1) & (generated == 1)).sum())
false_positive = int(((manual == 0) & (generated == 1)).sum())
false_negative = int(((manual == 1) & (generated == 0)).sum())
stem = manual_path.name.removesuffix("_matrix17.txt")
return MatrixMetrics(
stem=stem,
true_positive=true_positive,
false_positive=false_positive,
false_negative=false_negative,
manual_cells=int(manual.sum()),
generated_cells=int(generated.sum()),
manual_2x2=count_2x2_blocks(manual),
generated_2x2=count_2x2_blocks(generated),
)
def print_table(metrics: list[MatrixMetrics]) -> None:
header = (
"stem",
"P",
"R",
"F1",
"TP",
"FP",
"FN",
"manual",
"gen",
"manual_2x2",
"gen_2x2",
)
print(
f"{header[0]:42} {header[1]:>6} {header[2]:>6} {header[3]:>6} "
f"{header[4]:>4} {header[5]:>4} {header[6]:>4} {header[7]:>6} "
f"{header[8]:>6} {header[9]:>11} {header[10]:>8}"
)
for item in metrics:
print(
f"{item.stem:42} {item.precision:6.3f} {item.recall:6.3f} {item.f1:6.3f} "
f"{item.true_positive:4d} {item.false_positive:4d} {item.false_negative:4d} "
f"{item.manual_cells:6d} {item.generated_cells:6d} "
f"{item.manual_2x2:11d} {item.generated_2x2:8d}"
)
total_tp = sum(item.true_positive for item in metrics)
total_fp = sum(item.false_positive for item in metrics)
total_fn = sum(item.false_negative for item in metrics)
total_manual = sum(item.manual_cells for item in metrics)
total_generated = sum(item.generated_cells for item in metrics)
total_manual_2x2 = sum(item.manual_2x2 for item in metrics)
total_generated_2x2 = sum(item.generated_2x2 for item in metrics)
total = MatrixMetrics(
stem="TOTAL",
true_positive=total_tp,
false_positive=total_fp,
false_negative=total_fn,
manual_cells=total_manual,
generated_cells=total_generated,
manual_2x2=total_manual_2x2,
generated_2x2=total_generated_2x2,
)
print("-" * 113)
print(
f"{total.stem:42} {total.precision:6.3f} {total.recall:6.3f} {total.f1:6.3f} "
f"{total.true_positive:4d} {total.false_positive:4d} {total.false_negative:4d} "
f"{total.manual_cells:6d} {total.generated_cells:6d} "
f"{total.manual_2x2:11d} {total.generated_2x2:8d}"
)
def main() -> None:
parser = argparse.ArgumentParser(
description="Compare manual 17x17 labels against generated matrix output."
)
parser.add_argument(
"--manual-dir",
type=Path,
default=Path("PreProcessing/manual_matrices"),
)
parser.add_argument(
"--generated-dir",
type=Path,
default=Path("PreProcessing/processed_characters"),
)
parser.add_argument(
"--diff-dir",
type=Path,
default=Path("PreProcessing/manual_diffs"),
)
parser.add_argument("--scale", type=int, default=24)
parser.add_argument(
"--include-empty-manual",
action="store_true",
help="Include manual matrices with no black cells instead of treating them as unlabelled.",
)
args = parser.parse_args()
manual_paths = sorted(args.manual_dir.glob("*_matrix17.txt"))
if not manual_paths:
raise SystemExit(f"No manual matrices found in {args.manual_dir}")
args.diff_dir.mkdir(parents=True, exist_ok=True)
metrics: list[MatrixMetrics] = []
missing: list[Path] = []
skipped_empty: list[Path] = []
for manual_path in manual_paths:
manual = read_matrix(manual_path)
if not args.include_empty_manual and int(manual.sum()) == 0:
skipped_empty.append(manual_path)
continue
generated_path = args.generated_dir / manual_path.name
if not generated_path.exists():
missing.append(generated_path)
continue
generated = read_matrix(generated_path)
metrics.append(metrics_for_pair(manual_path, generated_path))
stem = manual_path.name.removesuffix("_matrix17.txt")
write_image(args.diff_dir / f"{stem}_diff.png", diff_image(manual, generated, args.scale))
if missing:
print("Missing generated matrices:")
for path in missing:
print(f" {path}")
if skipped_empty:
print("Skipped empty manual matrices:")
for path in skipped_empty:
print(f" {path}")
if not metrics:
raise SystemExit("No comparable matrix pairs found.")
print_table(metrics)
print(f"\nDiff images written to: {args.diff_dir}")
print("Diff colors: black=match, red=generated extra, blue=manual missing.")
if __name__ == "__main__":
main()

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from __future__ import annotations
import argparse
from pathlib import Path
import tkinter as tk
from tkinter import messagebox
import cv2
import numpy as np
IMAGE_SUFFIXES = {".png", ".jpg", ".jpeg", ".tif", ".tiff", ".bmp", ".webp"}
GRID_SIZE = 17
CELL_SIZE = 28
PREVIEW_SIZE = 560
def read_image(path: Path) -> np.ndarray:
raw = np.fromfile(str(path), dtype=np.uint8)
image = cv2.imdecode(raw, cv2.IMREAD_COLOR)
if image is None:
raise ValueError(f"Cannot read image: {path}")
return image
def write_image(path: Path, image: np.ndarray) -> None:
ok, encoded = cv2.imencode(path.suffix, image)
if not ok:
raise ValueError(f"Cannot encode image: {path}")
encoded.tofile(str(path))
def matrix_to_text(matrix: np.ndarray) -> str:
lines = []
for row in matrix:
lines.append("".join("1" if value else "0" for value in row))
return "\n".join(lines) + "\n"
def text_to_matrix(path: Path) -> np.ndarray:
matrix = np.zeros((GRID_SIZE, GRID_SIZE), dtype=np.uint8)
if not path.exists():
return matrix
rows = []
for line in path.read_text(encoding="utf-8").splitlines():
stripped = line.strip()
if not stripped or stripped.startswith("#"):
continue
rows.append(stripped)
if len(rows) == GRID_SIZE:
break
for row_index, row_text in enumerate(rows[:GRID_SIZE]):
for col_index, char in enumerate(row_text[:GRID_SIZE]):
matrix[row_index, col_index] = 1 if char == "1" else 0
return matrix
def matrix_to_png(matrix: np.ndarray, scale: int = 24) -> np.ndarray:
image = np.where(matrix > 0, 0, 255).astype(np.uint8)
return cv2.resize(
image,
(GRID_SIZE * scale, GRID_SIZE * scale),
interpolation=cv2.INTER_NEAREST,
)
class MatrixEditor:
def __init__(
self,
root: tk.Tk,
image_paths: list[Path],
output_dir: Path,
load_generated_dir: Path | None,
) -> None:
self.root = root
self.image_paths = image_paths
self.output_dir = output_dir
self.load_generated_dir = load_generated_dir
self.output_dir.mkdir(parents=True, exist_ok=True)
self.index = 0
self.matrix = np.zeros((GRID_SIZE, GRID_SIZE), dtype=np.uint8)
self.paint_value = 1
self.preview_photo: tk.PhotoImage | None = None
self.preview_temp = Path("/private/tmp/matrix17_editor_preview.png")
self.root.title("17x17 Matrix Editor")
self._build_ui()
self._bind_keys()
self.load_current_image()
@property
def image_path(self) -> Path:
return self.image_paths[self.index]
@property
def answer_path(self) -> Path:
return self.output_dir / f"{self.image_path.stem}_matrix17.txt"
@property
def answer_png_path(self) -> Path:
return self.output_dir / f"{self.image_path.stem}_matrix17.png"
def _build_ui(self) -> None:
main = tk.Frame(self.root)
main.pack(fill=tk.BOTH, expand=True, padx=10, pady=10)
left = tk.Frame(main)
left.pack(side=tk.LEFT, fill=tk.BOTH, expand=True)
right = tk.Frame(main)
right.pack(side=tk.LEFT, fill=tk.Y, padx=(12, 0))
self.preview_canvas = tk.Canvas(
left,
width=PREVIEW_SIZE,
height=PREVIEW_SIZE,
bg="white",
highlightthickness=1,
highlightbackground="#999",
)
self.preview_canvas.pack(fill=tk.BOTH, expand=True)
self.grid_canvas = tk.Canvas(
right,
width=GRID_SIZE * CELL_SIZE + 1,
height=GRID_SIZE * CELL_SIZE + 1,
bg="white",
highlightthickness=1,
highlightbackground="#777",
)
self.grid_canvas.pack()
self.grid_canvas.bind("<Button-1>", self._on_left_click)
self.grid_canvas.bind("<B1-Motion>", self._on_left_drag)
self.grid_canvas.bind("<Button-3>", self._on_right_click)
self.grid_canvas.bind("<B3-Motion>", self._on_right_drag)
buttons = tk.Frame(right)
buttons.pack(fill=tk.X, pady=(10, 0))
tk.Button(buttons, text="上一张", command=self.prev_image).grid(
row=0, column=0, sticky="ew", padx=2, pady=2
)
tk.Button(buttons, text="下一张", command=self.next_image).grid(
row=0, column=1, sticky="ew", padx=2, pady=2
)
tk.Button(buttons, text="保存", command=self.save_answer).grid(
row=1, column=0, sticky="ew", padx=2, pady=2
)
tk.Button(buttons, text="清空", command=self.clear_matrix).grid(
row=1, column=1, sticky="ew", padx=2, pady=2
)
tk.Button(buttons, text="载入生成结果", command=self.load_generated).grid(
row=2, column=0, columnspan=2, sticky="ew", padx=2, pady=2
)
buttons.columnconfigure(0, weight=1)
buttons.columnconfigure(1, weight=1)
self.status = tk.Label(right, text="", anchor="w", justify="left")
self.status.pack(fill=tk.X, pady=(8, 0))
def _bind_keys(self) -> None:
self.root.bind("<Left>", lambda _event: self.prev_image())
self.root.bind("<Right>", lambda _event: self.next_image())
self.root.bind("s", lambda _event: self.save_answer())
self.root.bind("S", lambda _event: self.save_answer())
self.root.bind("c", lambda _event: self.clear_matrix())
self.root.bind("C", lambda _event: self.clear_matrix())
self.root.bind("1", lambda _event: self.set_paint_value(1))
self.root.bind("0", lambda _event: self.set_paint_value(0))
def set_paint_value(self, value: int) -> None:
self.paint_value = 1 if value else 0
self._update_status()
def load_current_image(self) -> None:
self.matrix = text_to_matrix(self.answer_path)
self._draw_preview()
self._draw_grid()
self._update_status()
def _draw_preview(self) -> None:
image = read_image(self.image_path)
height, width = image.shape[:2]
scale = min(PREVIEW_SIZE / height, PREVIEW_SIZE / width)
new_width = max(1, int(round(width * scale)))
new_height = max(1, int(round(height * scale)))
resized = cv2.resize(image, (new_width, new_height), interpolation=cv2.INTER_NEAREST)
canvas = np.full((PREVIEW_SIZE, PREVIEW_SIZE, 3), 255, dtype=np.uint8)
x = (PREVIEW_SIZE - new_width) // 2
y = (PREVIEW_SIZE - new_height) // 2
canvas[y : y + new_height, x : x + new_width] = resized
write_image(self.preview_temp, canvas)
self.preview_photo = tk.PhotoImage(file=str(self.preview_temp))
self.preview_canvas.delete("all")
self.preview_canvas.create_image(0, 0, anchor="nw", image=self.preview_photo)
def _draw_grid(self) -> None:
self.grid_canvas.delete("all")
for row in range(GRID_SIZE):
for col in range(GRID_SIZE):
x1 = col * CELL_SIZE
y1 = row * CELL_SIZE
x2 = x1 + CELL_SIZE
y2 = y1 + CELL_SIZE
fill = "black" if self.matrix[row, col] else "white"
self.grid_canvas.create_rectangle(
x1,
y1,
x2,
y2,
fill=fill,
outline="#888",
)
for index in range(GRID_SIZE + 1):
pos = index * CELL_SIZE
width = 2 if index in (0, GRID_SIZE) else 1
self.grid_canvas.create_line(0, pos, GRID_SIZE * CELL_SIZE, pos, fill="#555", width=width)
self.grid_canvas.create_line(pos, 0, pos, GRID_SIZE * CELL_SIZE, fill="#555", width=width)
def _grid_cell_from_event(self, event: tk.Event) -> tuple[int, int] | None:
col = int(event.x // CELL_SIZE)
row = int(event.y // CELL_SIZE)
if 0 <= row < GRID_SIZE and 0 <= col < GRID_SIZE:
return row, col
return None
def _set_cell_from_event(self, event: tk.Event, value: int) -> None:
cell = self._grid_cell_from_event(event)
if cell is None:
return
row, col = cell
if self.matrix[row, col] == value:
return
self.matrix[row, col] = value
self._draw_grid()
self._update_status()
def _on_left_click(self, event: tk.Event) -> None:
cell = self._grid_cell_from_event(event)
if cell is None:
return
row, col = cell
self.paint_value = 0 if self.matrix[row, col] else 1
self.matrix[row, col] = self.paint_value
self._draw_grid()
self._update_status()
def _on_left_drag(self, event: tk.Event) -> None:
self._set_cell_from_event(event, self.paint_value)
def _on_right_click(self, event: tk.Event) -> None:
self.paint_value = 0
self._set_cell_from_event(event, 0)
self._update_status()
def _on_right_drag(self, event: tk.Event) -> None:
self._set_cell_from_event(event, 0)
def save_answer(self) -> None:
self.answer_path.write_text(matrix_to_text(self.matrix), encoding="utf-8")
write_image(self.answer_png_path, matrix_to_png(self.matrix))
self._update_status(saved=True)
def clear_matrix(self) -> None:
self.matrix[:, :] = 0
self._draw_grid()
self._update_status()
def load_generated(self) -> None:
if self.load_generated_dir is None:
messagebox.showinfo("提示", "未配置生成结果目录")
return
path = self.load_generated_dir / f"{self.image_path.stem}_matrix17.txt"
if not path.exists():
messagebox.showinfo("提示", f"找不到生成结果：\n{path}")
return
self.matrix = text_to_matrix(path)
self._draw_grid()
self._update_status()
def prev_image(self) -> None:
self.save_answer()
self.index = (self.index - 1) % len(self.image_paths)
self.load_current_image()
def next_image(self) -> None:
self.save_answer()
self.index = (self.index + 1) % len(self.image_paths)
self.load_current_image()
def _update_status(self, saved: bool = False) -> None:
filled = int(self.matrix.sum())
two_by_two = count_2x2_blocks(self.matrix)
saved_text = "已保存\n" if saved else ""
self.status.config(
text=(
f"{saved_text}"
f"{self.index + 1}/{len(self.image_paths)}\n"
f"{self.image_path.name}\n"
f"黑格：{filled}\n"
f"2x2 全黑：{two_by_two}\n"
f"左键：切换/拖拽绘制\n"
f"右键：擦除\n"
f"S 保存，←/→ 切换"
)
)
def count_2x2_blocks(matrix: np.ndarray) -> int:
image = matrix > 0
total = 0
for row in range(GRID_SIZE - 1):
for col in range(GRID_SIZE - 1):
total += int(image[row : row + 2, col : col + 2].sum() == 4)
return total
def main() -> None:
parser = argparse.ArgumentParser(description="Draw manual 17x17 matrix labels.")
parser.add_argument(
"--input-dir",
type=Path,
default=Path("PreProcessing/original_characters"),
)
parser.add_argument(
"--output-dir",
type=Path,
default=Path("PreProcessing/manual_matrices"),
)
parser.add_argument(
"--load-generated-dir",
type=Path,
default=Path("PreProcessing/processed_characters"),
)
args = parser.parse_args()
image_paths = sorted(
path
for path in args.input_dir.iterdir()
if path.is_file() and path.suffix.lower() in IMAGE_SUFFIXES
)
if not image_paths:
raise SystemExit(f"No images found in {args.input_dir}")
root = tk.Tk()
MatrixEditor(
root=root,
image_paths=image_paths,
output_dir=args.output_dir,
load_generated_dir=args.load_generated_dir,
)
root.mainloop()
if __name__ == "__main__":
main()

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from __future__ import annotations
import argparse
from pathlib import Path
import cv2
import numpy as np
from process_characters import (
IMAGE_SUFFIXES,
binarize_foreground,
clean_mask,
matrix_map,
mask_to_display,
read_image,
write_image,
)
def matrix_to_text(matrix: np.ndarray) -> str:
return "\n".join(
"".join("1" if value > 0 else "0" for value in row) for row in matrix
) + "\n"
def iter_image_paths(input_dir: Path) -> list[Path]:
return sorted(
path
for path in input_dir.iterdir()
if path.is_file() and path.suffix.lower() in IMAGE_SUFFIXES
)
def process_image(
image_path: Path,
cleaned_dir: Path,
matrix_dir: Path,
grid_size: int = 17,
cell_samples: int = 32,
open_kernel_size: int = 1,
close_kernel_size: int = 0,
median_size: int = 1,
min_component_area: int = 8,
triangle_threshold: float = 0.18,
) -> None:
original = read_image(image_path)
gray = cv2.cvtColor(original, cv2.COLOR_BGR2GRAY)
binary = binarize_foreground(gray)
cleaned = clean_mask(
binary,
open_kernel_size=open_kernel_size,
close_kernel_size=close_kernel_size,
median_size=median_size,
min_component_area=min_component_area,
grid_size=grid_size,
)
mapping = matrix_map(
cleaned,
grid_size=grid_size,
triangle_threshold=triangle_threshold,
cell_samples=cell_samples,
)
stem = image_path.stem
write_image(cleaned_dir / f"{stem}_cleaned.png", mask_to_display(cleaned))
(matrix_dir / f"{stem}_matrix.txt").write_text(
matrix_to_text(mapping.matrix),
encoding="utf-8",
)
def process_directory(
input_dir: Path,
cleaned_dir: Path,
matrix_dir: Path,
grid_size: int = 17,
cell_samples: int = 32,
open_kernel_size: int = 1,
close_kernel_size: int = 0,
median_size: int = 1,
min_component_area: int = 8,
triangle_threshold: float = 0.18,
) -> int:
cleaned_dir.mkdir(parents=True, exist_ok=True)
matrix_dir.mkdir(parents=True, exist_ok=True)
image_paths = iter_image_paths(input_dir)
if not image_paths:
raise SystemExit(f"No images found in {input_dir}")
for image_path in image_paths:
process_image(
image_path=image_path,
cleaned_dir=cleaned_dir,
matrix_dir=matrix_dir,
grid_size=grid_size,
cell_samples=cell_samples,
open_kernel_size=open_kernel_size,
close_kernel_size=close_kernel_size,
median_size=median_size,
min_component_area=min_component_area,
triangle_threshold=triangle_threshold,
)
print(f"processed: {image_path.name}")
return len(image_paths)
def build_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser(
description="Clean seal-script images and export 17x17 matrix text files."
)
parser.add_argument("--input-dir", type=Path, required=True)
parser.add_argument("--cleaned-dir", type=Path, required=True)
parser.add_argument("--matrix-dir", type=Path, required=True)
parser.add_argument("--grid-size", type=int, default=17)
parser.add_argument("--cell-samples", type=int, default=32)
parser.add_argument("--open-kernel-size", type=int, default=1)
parser.add_argument("--close-kernel-size", type=int, default=0)
parser.add_argument("--median-size", type=int, default=1)
parser.add_argument("--min-component-area", type=int, default=8)
parser.add_argument("--triangle-threshold", type=float, default=0.18)
return parser
def main() -> None:
args = build_parser().parse_args()
process_directory(
input_dir=args.input_dir,
cleaned_dir=args.cleaned_dir,
matrix_dir=args.matrix_dir,
grid_size=args.grid_size,
cell_samples=args.cell_samples,
open_kernel_size=args.open_kernel_size,
close_kernel_size=args.close_kernel_size,
median_size=args.median_size,
min_component_area=args.min_component_area,
triangle_threshold=args.triangle_threshold,
)
if __name__ == "__main__":
main()

186
PreProcessing/svg_characters/篆文字_page_017_text_17_pixelated.svg
View File

@@ -3,141 +3,55 @@
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