347 lines
13 KiB
Python
347 lines
13 KiB
Python
"""签名图片处理诊断脚本（独立调试用）。
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工作流:
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把待测试的图片 (.png / .jpg / .jpeg) 丢到 image_test/input/ 下，
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直接双击 run.bat 或 `python image_test/process.py` 即可。
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脚本会自动遍历 input/ 下所有图片，每张图都跑完整流水线并把
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每一步中间结果存到 image_test/output/<图片名>/ 子目录。
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参数调整: 直接改下方 CONFIG 段，不用命令行。
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输出结构 (每张图一个子目录):
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image_test/output/<stem>/
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00_original.png 原图
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01_binarized.png 二值化后（仅 is_signature=true 时）
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02_white_removed.png 白底转透明后
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03_border_cropped.png 透明边框裁剪后
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04_scaled.png 按高度等比缩放后
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05_noised.png 边缘噪声后
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06_rotated.png 旋转后
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07_final_cropped.png 紧致裁剪后（最终输出）
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final.png 最终输出（等价于插入 Word 的那张）
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diagnostics.txt 每步尺寸 / 内容像素占比 / bbox
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"""
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import io
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import random
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import shutil
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from pathlib import Path
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from PIL import Image, ImageChops
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# ============================================================================
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# 配置区：直接改这里，不用命令行
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# ============================================================================
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CONFIG = {
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# 处理对象
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"is_signature": True, # True: 手写签字(先二值化)；False: 普通签章(不二值化)
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# 处理参数（对应正式流水线的全局参数）
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"height_cm": 1, # 目标高度(cm)；None 表示不缩放
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"noise_level": 0, # 边缘噪声 0~10
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"rotate_min": 0, # 旋转最小角度
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"rotate_max": 0, # 旋转最大角度
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"dpi": 96, # 缩放 DPI
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# 算法阈值（出问题时优先调这几个）
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"alpha_threshold": 30, # 紧致裁剪: alpha 高于此值视为有内容
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"brightness_threshold": 210, # 紧致裁剪: 亮度低于此值视为暗（笔画）
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"binarize_threshold": 128, # 二值化阈值
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"white_threshold": 240, # 抠白底: RGB 都高于此值视为白底
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# 调试控制
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"stop_at": "all", # all / original / binarize / remove_white / crop_border / scale / noise / rotate
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"seed": 42, # 随机种子（让噪声/旋转结果可复现；None=随机）
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"clear_output": True, # 每次运行前清空 output 目录
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}
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# 输入输出目录（相对脚本位置，固定）
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BASE_DIR = Path(__file__).resolve().parent
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INPUT_DIR = BASE_DIR / "input"
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OUTPUT_DIR = BASE_DIR / "output"
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IMAGE_EXTS = {".png", ".jpg", ".jpeg"}
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# ============================================================================
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# 以下函数从 lib/image_processor.py 原样复制，方便独立调参
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# ============================================================================
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def binarize(img, threshold=128):
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"""手写签字黑白二值化：>threshold -> 255(白)，否则 0(黑)。"""
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if img.mode != 'L':
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img = img.convert('L')
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img = img.point(lambda p: 255 if p > threshold else 0)
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img = img.convert('RGBA')
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return img
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def remove_white_background(img, white_threshold=240):
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"""接近白色的像素转为全透明。"""
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if img.mode != 'RGBA':
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img = img.convert('RGBA')
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datas = img.getdata()
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new_data = []
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for item in datas:
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r, g, b, a = item
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if r > white_threshold and g > white_threshold and b > white_threshold:
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new_data.append((255, 255, 255, 0))
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else:
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new_data.append((r, g, b, a))
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img.putdata(new_data)
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return img
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def crop_transparent_border(img):
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"""按 alpha 通道 getbbox 裁掉四周完全透明区域。"""
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if img.mode != 'RGBA':
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img = img.convert('RGBA')
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bbox = img.getbbox()
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if bbox:
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img = img.crop(bbox)
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return img
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def scale_by_height(img, height_cm, dpi=96):
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"""按目标高度（厘米）等比缩放。"""
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try:
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from PIL.Image import Resampling
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resample = Resampling.LANCZOS
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except ImportError:
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resample = Image.LANCZOS
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target_h = int(height_cm * dpi / 2.54)
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if target_h <= 0:
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return img
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w, h = img.size
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if h == 0:
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return img
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ratio = target_h / h
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new_w = max(1, int(w * ratio))
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new_h = target_h
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return img.resize((new_w, new_h), resample)
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def add_edge_noise(img, noise_level):
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"""在笔画边缘添加随机噪点，模拟印章不均匀。"""
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if img.mode != 'RGBA':
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img = img.convert('RGBA')
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w, h = img.size
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pixels = img.load()
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intensity = noise_level * 2
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for x in range(w):
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for y in range(h):
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r, g, b, a = pixels[x, y]
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if a == 0:
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continue
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is_edge = False
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for dx in range(-2, 3):
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for dy in range(-2, 3):
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nx, ny = x + dx, y + dy
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if 0 <= nx < w and 0 <= ny < h:
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if pixels[nx, ny][3] == 0:
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is_edge = True
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break
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if is_edge:
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break
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if is_edge and random.random() < noise_level / 10.0:
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offset_x = random.randint(-intensity, intensity)
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offset_y = random.randint(-intensity, intensity)
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nx = x + offset_x
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ny = y + offset_y
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if 0 <= nx < w and 0 <= ny < h:
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nr = min(255, max(0, r + random.randint(-20, 20)))
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ng = min(255, max(0, g + random.randint(-20, 20)))
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nb = min(255, max(0, b + random.randint(-20, 20)))
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na = min(255, max(0, a + random.randint(-30, 10)))
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if pixels[nx, ny][3] == 0:
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pixels[nx, ny] = (nr, ng, nb, na)
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return img
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def rotate_image(img, rotate_min, rotate_max):
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"""随机角度旋转，expand=True 扩展画布以容纳旋转后内容。"""
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try:
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from PIL.Image import Resampling
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resample = Resampling.BICUBIC
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except ImportError:
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resample = Image.BICUBIC
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if rotate_min > rotate_max:
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rotate_min, rotate_max = rotate_max, rotate_min
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angle = random.randint(rotate_min, rotate_max)
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return img.rotate(angle, resample=resample, expand=True,
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fillcolor=(0, 0, 0, 0))
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def crop_to_content(img, alpha_threshold=30, brightness_threshold=210):
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"""末尾紧致裁剪：(alpha > th) AND (亮度 < th)。"""
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if img.mode != 'RGBA':
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img = img.convert('RGBA')
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r, g, b, a = img.split()
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luma = Image.merge('RGB', (r, g, b)).convert('L')
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alpha_mask = a.point(lambda p: 255 if p > alpha_threshold else 0)
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luma_mask = luma.point(lambda p: 255 if p < brightness_threshold else 0)
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combined = ImageChops.darker(alpha_mask, luma_mask)
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bbox = combined.getbbox()
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if bbox and bbox != (0, 0, img.size[0], img.size[1]):
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img = img.crop(bbox)
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return img, bbox
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# ============================================================================
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# 诊断辅助
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# ============================================================================
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def content_stats(img, alpha_threshold=30, brightness_threshold=210):
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if img.mode != 'RGBA':
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img = img.convert('RGBA')
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r, g, b, a = img.split()
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luma = Image.merge('RGB', (r, g, b)).convert('L')
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alpha_mask = a.point(lambda p: 255 if p > alpha_threshold else 0)
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luma_mask = luma.point(lambda p: 255 if p < brightness_threshold else 0)
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combined = ImageChops.darker(alpha_mask, luma_mask)
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total = img.size[0] * img.size[1] or 1
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ha, hl, hc = alpha_mask.histogram(), luma_mask.histogram(), combined.histogram()
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return {
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'size': img.size,
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'alpha_pct': 100.0 * sum(ha[1:]) / total,
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'dark_pct': 100.0 * sum(hl[1:]) / total,
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'content_pct': 100.0 * sum(hc[1:]) / total,
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'bbox': combined.getbbox(),
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}
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def save_step(out_dir, idx, name, img, diag_lines, alpha_th, bright_th):
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if img is None:
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diag_lines.append(f"[{idx:02d}] {name:18s} SKIPPED")
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return
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path = out_dir / f"{idx:02d}_{name}.png"
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img.save(path)
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s = content_stats(img, alpha_th, bright_th)
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line = (f"[{idx:02d}] {name:18s} size={str(s['size']):14s} "
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f"alpha={s['alpha_pct']:5.1f}% dark={s['dark_pct']:5.1f}% "
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f"content={s['content_pct']:5.1f}% bbox={s['bbox']}")
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diag_lines.append(line)
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# ============================================================================
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# 单张图处理
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# ============================================================================
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def process_one(input_path, out_dir, cfg):
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out_dir = Path(out_dir)
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out_dir.mkdir(parents=True, exist_ok=True)
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diag = []
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img = Image.open(input_path)
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diag.append(f"输入: {input_path.name}")
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diag.append(f"模式: {img.mode} 尺寸: {img.size}")
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diag.append(f"参数: is_sig={cfg['is_signature']} height_cm={cfg['height_cm']} "
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f"noise={cfg['noise_level']} rotate=[{cfg['rotate_min']},{cfg['rotate_max']}] dpi={cfg['dpi']}")
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diag.append(f"阈值: alpha>{cfg['alpha_threshold']} 亮度<{cfg['brightness_threshold']} "
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f"二值化>{cfg['binarize_threshold']} 白底>{cfg['white_threshold']}")
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diag.append("-" * 80)
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save_step(out_dir, 0, "original", img, diag,
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cfg['alpha_threshold'], cfg['brightness_threshold'])
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if cfg['stop_at'] == 'original':
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return diag
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if cfg['is_signature']:
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img = binarize(img, threshold=cfg['binarize_threshold'])
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save_step(out_dir, 1, "binarized",
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img if cfg['is_signature'] else None, diag,
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cfg['alpha_threshold'], cfg['brightness_threshold'])
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if cfg['stop_at'] == 'binarize':
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return diag
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img = remove_white_background(img, white_threshold=cfg['white_threshold'])
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save_step(out_dir, 2, "white_removed", img, diag,
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cfg['alpha_threshold'], cfg['brightness_threshold'])
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if cfg['stop_at'] == 'remove_white':
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return diag
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img = crop_transparent_border(img)
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save_step(out_dir, 3, "border_cropped", img, diag,
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cfg['alpha_threshold'], cfg['brightness_threshold'])
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if cfg['stop_at'] == 'crop_border':
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return diag
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h = cfg['height_cm']
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if h is not None and h > 0:
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img = scale_by_height(img, h, dpi=cfg['dpi'])
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save_step(out_dir, 4, "scaled", img if (h is not None and h > 0) else None,
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diag, cfg['alpha_threshold'], cfg['brightness_threshold'])
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if cfg['stop_at'] == 'scale':
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return diag
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if cfg['noise_level'] > 0:
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img = add_edge_noise(img, cfg['noise_level'])
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save_step(out_dir, 5, "noised", img if cfg['noise_level'] > 0 else None,
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diag, cfg['alpha_threshold'], cfg['brightness_threshold'])
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if cfg['stop_at'] == 'noise':
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return diag
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if cfg['rotate_min'] != 0 or cfg['rotate_max'] != 0:
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img = rotate_image(img, cfg['rotate_min'], cfg['rotate_max'])
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save_step(out_dir, 6, "rotated",
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img if (cfg['rotate_min'] != 0 or cfg['rotate_max'] != 0) else None,
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diag, cfg['alpha_threshold'], cfg['brightness_threshold'])
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if cfg['stop_at'] == 'rotate':
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return diag
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img, _ = crop_to_content(img,
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alpha_threshold=cfg['alpha_threshold'],
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brightness_threshold=cfg['brightness_threshold'])
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save_step(out_dir, 7, "final_cropped", img, diag,
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cfg['alpha_threshold'], cfg['brightness_threshold'])
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final_buf = io.BytesIO()
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img.save(final_buf, format='PNG')
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with open(out_dir / "final.png", 'wb') as f:
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f.write(final_buf.getvalue())
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diag.append("-" * 80)
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diag.append(f"最终输出: final.png size={img.size}")
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return diag
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# ============================================================================
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# 主入口：扫描 input/ 下所有图片
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# ============================================================================
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def main():
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if not INPUT_DIR.exists():
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INPUT_DIR.mkdir(parents=True, exist_ok=True)
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if CONFIG.get('seed') is not None:
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random.seed(CONFIG['seed'])
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images = sorted([p for p in INPUT_DIR.iterdir()
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if p.suffix.lower() in IMAGE_EXTS and p.is_file()])
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if not images:
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print(f"未在 {INPUT_DIR} 下找到 png/jpg 图片，请先把测试图片放进去")
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return
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if CONFIG.get('clear_output') and OUTPUT_DIR.exists():
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shutil.rmtree(OUTPUT_DIR)
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OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
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print(f"找到 {len(images)} 张图: {[p.name for p in images]}")
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print(f"输出目录: {OUTPUT_DIR}\n")
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for img_path in images:
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out_sub = OUTPUT_DIR / img_path.stem
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print(f"=== 处理: {img_path.name} ===")
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try:
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diag = process_one(img_path, out_sub, CONFIG)
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for line in diag:
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print(line)
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with open(out_sub / "diagnostics.txt", 'w', encoding='utf-8') as f:
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f.write("\n".join(diag))
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print(f"-> 输出: {out_sub}\n")
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except Exception as e:
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print(f"处理失败: {e}\n")
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if __name__ == '__main__':
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main()
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