Hua
9b5f29083e
New solver subsystem with independent models: - GapDetectorCNN (1x128x256 grayscale → sigmoid) for slide gap detection - RotationRegressor (3x128x128 RGB → sin/cos via tanh) for rotation angle prediction - SlideSolver with 3-tier strategy: template match → edge detect → CNN fallback - RotateSolver with ONNX sin/cos → atan2 inference - Generators, training scripts, CLI commands, and slide track utility Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
113 lines
3.7 KiB
Python
113 lines
3.7 KiB
Python
"""
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滑块验证码数据生成器
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生成滑块验证码训练数据：随机纹理/色块背景 + 方形缺口 + 阴影效果。
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标签 = 缺口中心 x 坐标 (整数)
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文件名格式: {gap_x}_{index:06d}.png
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"""
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import random
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from PIL import Image, ImageDraw, ImageFilter
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from config import SOLVER_CONFIG
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from generators.base import BaseCaptchaGenerator
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class SlideDataGenerator(BaseCaptchaGenerator):
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"""滑块验证码数据生成器。"""
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def __init__(self, seed: int | None = None):
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from config import RANDOM_SEED
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super().__init__(seed=seed if seed is not None else RANDOM_SEED)
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self.cfg = SOLVER_CONFIG["slide"]
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self.height, self.width = self.cfg["cnn_input_size"] # (H, W)
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self.gap_size = 40 # 缺口大小
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def generate(self, text: str | None = None) -> tuple[Image.Image, str]:
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rng = self.rng
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gs = self.gap_size
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# 缺口 x 范围: 留出边距
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margin = gs + 10
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gap_x = rng.randint(margin, self.width - margin)
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gap_y = rng.randint(10, self.height - gs - 10)
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if text is None:
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text = str(gap_x)
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# 1. 生成纹理背景
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img = self._textured_background(rng)
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# 2. 绘制缺口 (半透明灰色区域 + 阴影)
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overlay = Image.new("RGBA", img.size, (0, 0, 0, 0))
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overlay_draw = ImageDraw.Draw(overlay)
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# 阴影 (稍大一圈)
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overlay_draw.rectangle(
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[gap_x + 2, gap_y + 2, gap_x + gs + 2, gap_y + gs + 2],
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fill=(0, 0, 0, 60),
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)
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# 缺口本体
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overlay_draw.rectangle(
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[gap_x, gap_y, gap_x + gs, gap_y + gs],
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fill=(80, 80, 80, 160),
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outline=(60, 60, 60, 200),
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width=2,
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)
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img = img.convert("RGBA")
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img = Image.alpha_composite(img, overlay)
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img = img.convert("RGB")
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# 3. 轻微模糊
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img = img.filter(ImageFilter.GaussianBlur(radius=0.3))
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return img, text
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def _textured_background(self, rng: random.Random) -> Image.Image:
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"""生成带纹理的彩色背景。"""
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img = Image.new("RGB", (self.width, self.height))
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draw = ImageDraw.Draw(img)
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# 渐变底色
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base_r = rng.randint(80, 200)
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base_g = rng.randint(80, 200)
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base_b = rng.randint(80, 200)
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for y in range(self.height):
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ratio = y / max(self.height - 1, 1)
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r = int(base_r + 40 * ratio)
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g = int(base_g - 20 * ratio)
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b = int(base_b + 20 * ratio)
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r, g, b = max(0, min(255, r)), max(0, min(255, g)), max(0, min(255, b))
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draw.line([(0, y), (self.width, y)], fill=(r, g, b))
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# 纹理噪声
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for _ in range(self.width * self.height // 6):
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x = rng.randint(0, self.width - 1)
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y = rng.randint(0, self.height - 1)
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pixel = img.getpixel((x, y))
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noise = tuple(
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max(0, min(255, c + rng.randint(-30, 30)))
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for c in pixel
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)
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draw.point((x, y), fill=noise)
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# 随机色块 (模拟图案)
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for _ in range(rng.randint(4, 8)):
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x1, y1 = rng.randint(0, self.width - 30), rng.randint(0, self.height - 20)
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x2, y2 = x1 + rng.randint(15, 50), y1 + rng.randint(10, 30)
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color = tuple(rng.randint(50, 230) for _ in range(3))
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draw.rectangle([x1, y1, x2, y2], fill=color)
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# 随机圆形
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for _ in range(rng.randint(2, 5)):
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cx = rng.randint(10, self.width - 10)
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cy = rng.randint(10, self.height - 10)
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cr = rng.randint(5, 20)
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color = tuple(rng.randint(50, 230) for _ in range(3))
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draw.ellipse([cx - cr, cy - cr, cx + cr, cy + cr], fill=color)
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return img
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