diff --git a/.gitignore b/.gitignore index f3183b4..f76dbe6 100644 --- a/.gitignore +++ b/.gitignore @@ -22,3 +22,5 @@ __pycache__/ tcaptcha/data/raw/ tcaptcha/data/crops/ data/ +runs/ +yolov8n.pt diff --git a/pyproject.toml b/pyproject.toml index 6aa71ce..97ea410 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,7 +3,10 @@ name = "hello-js-reverse-skill" version = "0.1.0" requires-python = ">=3.12" dependencies = [ + "ddddocr>=1.6.1", "httpx>=0.28.1", "numpy>=2.5.3", + "opencv-python-headless>=5.0.0.93", "pillow>=12.3.0", + "ultralytics>=8.4.147", ] diff --git a/tcaptcha/evaluate.py b/tcaptcha/evaluate.py new file mode 100644 index 0000000..03598be --- /dev/null +++ b/tcaptcha/evaluate.py @@ -0,0 +1,147 @@ +""" +腾讯天御点选验证码求解器端到端评估脚本 +评估指标: +1. 单点命中率 (Hit Rate per Point, distance <= 30px) +2. 无序3点命中率 (All 3 icons detected regardless of order) +3. 严格时序三点命中率 (Full 3-point Sequence Accuracy: P1->T1, P2->T2, P3->T3) +""" + +import json +import logging +import sys +from pathlib import Path + +# 确保项目根目录在 sys.path 中 +PROJECT_ROOT = Path(__file__).resolve().parent.parent +if str(PROJECT_ROOT) not in sys.path: + sys.path.insert(0, str(PROJECT_ROOT)) + +import numpy as np +from tcaptcha.solver import CaptchaSolver + +logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s") +logger = logging.getLogger("evaluate") + + +def evaluate_solver(labels_path: Path = Path("tcaptcha/data/labels.jsonl"), tolerance_px: float = 35.0): + if not labels_path.exists(): + logger.error(f"Labels file not found: {labels_path}") + return + + solver = CaptchaSolver() + + samples = [] + with open(labels_path, "r", encoding="utf-8") as f: + for line in f: + if line.strip(): + samples.append(json.loads(line.strip())) + + total_samples = len(samples) + total_points = total_samples * 3 + + exact_seq_success = 0 + any_order_success = 0 + total_points_hit = 0 + point_errors = [] # distances for hits + + failed_samples = [] + + print(f"\n================ 开始评估 (共 {total_samples} 个样本) ================\n") + + for idx, s in enumerate(samples, 1): + sid = s["sample_id"] + gt_points = [(p["x"], p["y"]) for p in s["points"]] + bg = Path(f"tcaptcha/data/raw/{sid}_bg.png") + prompt = Path(f"tcaptcha/data/raw/{sid}_prompt.png") + + if not bg.exists() or not prompt.exists(): + logger.warning(f"[{idx}/{total_samples}] {sid} 缺失图像文件") + continue + + pred_points = solver.solve(bg, prompt) + if len(pred_points) < 3: + logger.warning(f"[{idx}/{total_samples}] {sid} 预测点数不足3个: {pred_points}") + failed_samples.append((sid, "insufficient_points", gt_points, pred_points)) + continue + + # 1. 严格顺序匹配评估 + seq_hit = True + sample_point_hits = 0 + distances = [] + for i in range(3): + gx, gy = gt_points[i] + px, py = pred_points[i] + dist = np.hypot(px - gx, py - gy) + distances.append(dist) + if dist <= tolerance_px: + sample_point_hits += 1 + point_errors.append(dist) + else: + seq_hit = False + + total_points_hit += sample_point_hits + + # 2. 无序匹配评估 (检查是否3个图标都找出来了，哪怕顺序搞错) + # 为每个预测点找最近的GT点 + used_gt = set() + for px, py in pred_points: + best_gt = None + min_d = 9999 + for gi, (gx, gy) in enumerate(gt_points): + if gi in used_gt: + continue + d = np.hypot(px - gx, py - gy) + if d < min_d: + min_d = d + best_gt = gi + if best_gt is not None and min_d <= tolerance_px: + used_gt.add(best_gt) + + all_detected = (len(used_gt) == 3) + if all_detected: + any_order_success += 1 + + if seq_hit: + exact_seq_success += 1 + status = "✅ PASS" + else: + status = "❌ FAIL" + failed_samples.append((sid, f"dist={[round(d,1) for d in distances]}", gt_points, pred_points)) + + print(f"[{idx:02d}/{total_samples}] {sid}: {status} | 点命中: {sample_point_hits}/3 | 距离: {[round(d, 1) for d in distances]}") + + # 统计总结 + seq_acc = exact_seq_success / total_samples * 100 + order_agnostic_acc = any_order_success / total_samples * 100 + pt_hit_rate = total_points_hit / total_points * 100 + avg_err = np.mean(point_errors) if point_errors else 0.0 + + print("\n" + "=" * 60) + print("🎯 评估结果报告 (Evaluation Report):") + print(f" - 样本总数 (Total Samples): {total_samples}") + print(f" - 判定容差半径 (Tolerance): <= {tolerance_px} px") + print(f" - 单点命中率 (Point Hit Rate): {total_points_hit}/{total_points} ({pt_hit_rate:.2f}%)") + print(f" - 无序召回率 (Order-agnostic 3-Point Hit Rate): {any_order_success}/{total_samples} ({order_agnostic_acc:.2f}%)") + print(f" - 🌟 严格时序三点识别率 (Sequence Accuracy): {exact_seq_success}/{total_samples} ({seq_acc:.2f}%)") + print(f" - 命中点平均中心偏差 (Avg Center Offset): {avg_err:.2f} px") + print("=" * 60) + + if failed_samples: + print(f"\n❌ 失败样本分析 (共 {len(failed_samples)} 个):") + for sid, reason, gt, pred in failed_samples[:10]: + print(f" - {sid}: {reason} | GT={gt} vs Pred={pred}") + if len(failed_samples) > 10: + print(f" ... 还有 {len(failed_samples) - 10} 个失败样本未列出") + + return { + "seq_acc": seq_acc, + "order_agnostic_acc": order_agnostic_acc, + "pt_hit_rate": pt_hit_rate, + "failed_count": len(failed_samples) + } + + +if __name__ == "__main__": + import sys + tol = float(sys.argv[1]) if len(sys.argv) > 1 else 35.0 + evaluate_solver(tolerance_px=tol) diff --git a/tcaptcha/models/icon_classifier.pt b/tcaptcha/models/icon_classifier.pt new file mode 100644 index 0000000..3545b26 Binary files /dev/null and b/tcaptcha/models/icon_classifier.pt differ diff --git a/tcaptcha/models/siamese_matcher.pt b/tcaptcha/models/siamese_matcher.pt new file mode 100644 index 0000000..49f2c76 Binary files /dev/null and b/tcaptcha/models/siamese_matcher.pt differ diff --git a/tcaptcha/models/yolo_detector.pt b/tcaptcha/models/yolo_detector.pt new file mode 100644 index 0000000..e27ef9c Binary files /dev/null and b/tcaptcha/models/yolo_detector.pt differ diff --git a/tcaptcha/prepare_yolo.py b/tcaptcha/prepare_yolo.py new file mode 100644 index 0000000..8b731ad --- /dev/null +++ b/tcaptcha/prepare_yolo.py @@ -0,0 +1,144 @@ +""" +将标注的 55 组样本转换为标准 YOLO 目标检测数据集 +生成: + tcaptcha/data/yolo/images/train/ + tcaptcha/data/yolo/images/val/ + tcaptcha/data/yolo/labels/train/ + tcaptcha/data/yolo/labels/val/ + tcaptcha/data/yolo/dataset.yaml +""" + +import json +import logging +import random +import shutil +from pathlib import Path +import cv2 +import numpy as np + +logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s") +logger = logging.getLogger("prepare_yolo") + +RAW_DIR = Path("tcaptcha/data/raw") +LABEL_FILE = Path("tcaptcha/data/labels.jsonl") +YOLO_DIR = Path("tcaptcha/data/yolo") + +IMG_W = 672 +IMG_H = 480 +BOX_SIZE = 54 # 图标大致大小 (约 54x54 像素) + + +def find_all_icons_in_bg(bg_path: Path, labeled_points: list) -> list: + """ + 结合人工标注点与轮廓补充检测，找到大图中所有图标（包含 3 个目标 + 2~3 个干扰图标） + 返回: [(cx, cy, w, h), ...] 绝对像素坐标 + """ + img = cv2.imread(str(bg_path)) + if img is None: + return [] + gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + + # 提取暗色轮廓找干扰图标 + edges = cv2.Canny(gray, 40, 120) + kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 9)) + closed = cv2.morphologyEx(edges, cv2.MORPH_CLOSE, kernel) + contours, _ = cv2.findContours(closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + + all_boxes = [] + # 1. 先加入人工标注的 3 个目标（绝对精准） + for p in labeled_points: + cx, cy = p["x"], p["y"] + all_boxes.append((cx, cy, BOX_SIZE, BOX_SIZE)) + + # 2. 补充检测未标注的干扰图标 + for cnt in contours: + x, y, w, h = cv2.boundingRect(cnt) + if 26 <= w <= 85 and 26 <= h <= 85 and (w * h) >= 650: + cx, cy = x + w // 2, y + h // 2 + # 必须远离已有图标 + if all(np.hypot(cx - lx, cy - ly) > 42 for lx, ly, _, _ in all_boxes): + # 限制尺寸 + bw = min(68, max(46, w + 8)) + bh = min(68, max(46, h + 8)) + all_boxes.append((cx, cy, bw, bh)) + + return all_boxes + + +def prepare_dataset(val_ratio: float = 0.2, seed: int = 42): + random.seed(seed) + if not LABEL_FILE.exists(): + logger.error("标签文件不存在: %s", LABEL_FILE) + return + + # 清理并创建目录 + if YOLO_DIR.exists(): + shutil.rmtree(YOLO_DIR) + + for split in ["train", "val"]: + (YOLO_DIR / "images" / split).mkdir(parents=True, exist_ok=True) + (YOLO_DIR / "labels" / split).mkdir(parents=True, exist_ok=True) + + with open(LABEL_FILE, "r", encoding="utf-8") as f: + records = [json.loads(line) for line in f if line.strip()] + + random.shuffle(records) + val_count = max(5, int(len(records) * val_ratio)) + train_records = records[val_count:] + val_records = records[:val_count] + + logger.info("总标注样本: %d (训练集: %d, 验证集: %d)", len(records), len(train_records), len(val_records)) + + def process_split(record_list, split_name): + total_boxes = 0 + for r in record_list: + sid = r["sample_id"] + src_img = RAW_DIR / f"{sid}_bg.png" + if not src_img.exists(): + continue + + dst_img = YOLO_DIR / "images" / split_name / f"{sid}.png" + shutil.copy(src_img, dst_img) + + # 提取全部图标坐标 + boxes = find_all_icons_in_bg(src_img, r["points"]) + total_boxes += len(boxes) + + # 写入 YOLO 格式标注: class_id x_center y_center width height (归一化到 0~1) + dst_label = YOLO_DIR / "labels" / split_name / f"{sid}.txt" + lines = [] + for cx, cy, bw, bh in boxes: + # 边界截断保护 + x1 = max(0, cx - bw / 2) + y1 = max(0, cy - bh / 2) + x2 = min(IMG_W, cx + bw / 2) + y2 = min(IMG_H, cy + bh / 2) + + norm_cx = (x1 + x2) / 2.0 / IMG_W + norm_cy = (y1 + y2) / 2.0 / IMG_H + norm_w = (x2 - x1) / IMG_W + norm_h = (y2 - y1) / IMG_H + + lines.append(f"0 {norm_cx:.6f} {norm_cy:.6f} {norm_w:.6f} {norm_h:.6f}") + + dst_label.write_text("\n".join(lines), encoding="utf-8") + logger.info(" [%s] 处理完成: %d 张图片, 共 %d 个图标标注", split_name, len(record_list), total_boxes) + + process_split(train_records, "train") + process_split(val_records, "val") + + # 生成 dataset.yaml + yaml_content = f"""path: {YOLO_DIR.resolve()} +train: images/train +val: images/val + +names: + 0: icon +""" + yaml_path = YOLO_DIR / "dataset.yaml" + yaml_path.write_text(yaml_content, encoding="utf-8") + logger.info("✓ YOLO 数据集生成完毕: %s", yaml_path) + + +if __name__ == "__main__": + prepare_dataset() diff --git a/tcaptcha/siamese.py b/tcaptcha/siamese.py new file mode 100644 index 0000000..20337ff --- /dev/null +++ b/tcaptcha/siamese.py @@ -0,0 +1,228 @@ +""" +图标孪生神经网络 (Icon Siamese Network) +用于计算提示图图标与背景候选图标之间的形状相似度（抗背景噪点、抗旋转） +""" + +import json +import logging +import random +from pathlib import Path +from typing import List, Tuple + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchvision.transforms as transforms +from PIL import Image +from torch.utils.data import DataLoader, Dataset + +import sys + +PROJECT_ROOT = Path(__file__).resolve().parent.parent +if str(PROJECT_ROOT) not in sys.path: + sys.path.insert(0, str(PROJECT_ROOT)) + +from tcaptcha.slicer import slice_prompt_image + +logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s") +logger = logging.getLogger("siamese") + +MODEL_PATH = PROJECT_ROOT / "tcaptcha/models/siamese_matcher.pt" +LABEL_FILE = PROJECT_ROOT / "tcaptcha/data/labels.jsonl" +RAW_DIR = PROJECT_ROOT / "tcaptcha/data/raw" +CROPS_DIR = PROJECT_ROOT / "tcaptcha/data/crops" + + +class IconSiameseNet(nn.Module): + """轻量级 CNN 孪生特征提取网络""" + + def __init__(self, emb_dim: int = 128): + super().__init__() + self.encoder = nn.Sequential( + nn.Conv2d(3, 32, kernel_size=3, padding=1), + nn.BatchNorm2d(32), + nn.LeakyReLU(0.1, inplace=True), + nn.MaxPool2d(2, 2), # 32x32 + nn.Conv2d(32, 64, kernel_size=3, padding=1), + nn.BatchNorm2d(64), + nn.LeakyReLU(0.1, inplace=True), + nn.MaxPool2d(2, 2), # 16x16 + nn.Conv2d(64, 128, kernel_size=3, padding=1), + nn.BatchNorm2d(128), + nn.LeakyReLU(0.1, inplace=True), + nn.MaxPool2d(2, 2), # 8x8 + nn.Conv2d(128, 256, kernel_size=3, padding=1), + nn.BatchNorm2d(256), + nn.LeakyReLU(0.1, inplace=True), + nn.AdaptiveAvgPool2d((1, 1)), # 256x1x1 + ) + self.head = nn.Sequential( + nn.Flatten(), + nn.Linear(256, emb_dim), + ) + + def forward_once(self, x: torch.Tensor) -> torch.Tensor: + feat = self.encoder(x) + emb = self.head(feat) + return F.normalize(emb, p=2, dim=1) + + def forward(self, x1: torch.Tensor, x2: torch.Tensor) -> torch.Tensor: + e1 = self.forward_once(x1) + e2 = self.forward_once(x2) + # 余弦相似度 [-1, 1] 映射到 [0, 1] + cosine = (e1 * e2).sum(dim=1) + return (cosine + 1.0) / 2.0 + + +class IconPairDataset(Dataset): + """构建 (Prompt图标, Target图块) 正负样本对""" + + def __init__(self, pairs: List[Tuple[Image.Image, Image.Image, int]], transform=None): + self.pairs = pairs + self.transform = transform + + def __len__(self): + return len(self.pairs) + + def __getitem__(self, idx): + img1, img2, label = self.pairs[idx] + if self.transform: + img1 = self.transform(img1) + img2 = self.transform(img2) + return img1, img2, torch.tensor(label, dtype=torch.float32) + + +def load_pairs_from_labels(): + if not LABEL_FILE.exists(): + return [], [] + + with open(LABEL_FILE, "r", encoding="utf-8") as f: + records = [json.loads(line) for line in f if line.strip()] + + all_prompts = [] + all_targets = [] + + for r in records: + sid = r["sample_id"] + prompt_path = RAW_DIR / f"{sid}_prompt.png" + if not prompt_path.exists(): + continue + + try: + p_crops = slice_prompt_image(prompt_path) + for i in range(3): + t_crop_path = CROPS_DIR / f"{sid}_target_{i+1}.png" + if t_crop_path.exists() and i < len(p_crops): + p_img = p_crops[i][0].convert("RGB") + t_img = Image.open(t_crop_path).convert("RGB") + all_prompts.append(p_img) + all_targets.append(t_img) + except Exception as e: + logger.warning("解析样本 %s 异常: %s", sid, e) + + return all_prompts, all_targets + + +def build_augmented_pairs(prompts, targets, num_neg_per_pos: int = 3): + pairs = [] + n = len(prompts) + for i in range(n): + # 正样本对 (label = 1) + pairs.append((prompts[i], targets[i], 1)) + + # 负样本对 (label = 0) + for _ in range(num_neg_per_pos): + neg_idx = random.choice([j for j in range(n) if j != i]) + pairs.append((prompts[i], targets[neg_idx], 0)) + + random.shuffle(pairs) + return pairs + + +def train_siamese(epochs: int = 25, batch_size: int = 32, lr: float = 0.001): + MODEL_PATH.parent.mkdir(parents=True, exist_ok=True) + device = torch.device("mps" if torch.backends.mps.is_available() else "cpu") + logger.info("采用训练加速设备: %s", device) + + prompts, targets = load_pairs_from_labels() + logger.info("读取到 %d 组高质量正样本对应关系", len(prompts)) + if len(prompts) < 10: + logger.error("标注样本不足，无法训练孪生网络") + return + + # 数据集划分 (80% 训练, 20% 验证) + indices = list(range(len(prompts))) + random.shuffle(indices) + split = int(len(prompts) * 0.8) + + train_p = [prompts[i] for i in indices[:split]] + train_t = [targets[i] for i in indices[:split]] + + val_p = [prompts[i] for i in indices[split:]] + val_t = [targets[i] for i in indices[split:]] + + train_pairs = build_augmented_pairs(train_p, train_t, num_neg_per_pos=3) + val_pairs = build_augmented_pairs(val_p, val_t, num_neg_per_pos=3) + + train_transform = transforms.Compose([ + transforms.Resize((64, 64)), + transforms.RandomRotation(degrees=45), # 强化抗旋转 + transforms.ColorJitter(brightness=0.2, contrast=0.2), + transforms.ToTensor(), + transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]), + ]) + + val_transform = transforms.Compose([ + transforms.Resize((64, 64)), + transforms.ToTensor(), + transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]), + ]) + + train_loader = DataLoader(IconPairDataset(train_pairs, train_transform), batch_size=batch_size, shuffle=True) + val_loader = DataLoader(IconPairDataset(val_pairs, val_transform), batch_size=batch_size, shuffle=False) + + model = IconSiameseNet(emb_dim=128).to(device) + criterion = nn.BCELoss() + optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=1e-4) + + logger.info("开始训练孪生图标比对网络 (Train: %d 对, Val: %d 对)...", len(train_pairs), len(val_pairs)) + + best_val_acc = 0.0 + for epoch in range(1, epochs + 1): + model.train() + train_loss = 0.0 + for x1, x2, y in train_loader: + x1, x2, y = x1.to(device), x2.to(device), y.to(device) + optimizer.zero_grad() + pred = model(x1, x2) + loss = criterion(pred, y) + loss.backward() + optimizer.step() + train_loss += loss.item() + + # 验证 + model.eval() + correct = 0 + total = 0 + with torch.no_grad(): + for x1, x2, y in val_loader: + x1, x2, y = x1.to(device), x2.to(device), y.to(device) + pred = model(x1, x2) + predicted_labels = (pred > 0.5).float() + correct += (predicted_labels == y).sum().item() + total += y.size(0) + + val_acc = correct / total if total > 0 else 0 + if val_acc > best_val_acc: + best_val_acc = val_acc + torch.save(model.state_dict(), MODEL_PATH) + + if epoch % 5 == 0 or epoch == epochs: + logger.info("Epoch [%d/%d] Loss: %.4f | Val Pair Acc: %.2f%% (Best: %.2f%%)", epoch, epochs, train_loss / len(train_loader), val_acc * 100, best_val_acc * 100) + + logger.info("✓ 孪生网络训练完成，最佳权重已保存至: %s", MODEL_PATH) + + +if __name__ == "__main__": + train_siamese(epochs=30) diff --git a/tcaptcha/solver.py b/tcaptcha/solver.py new file mode 100644 index 0000000..04c7751 --- /dev/null +++ b/tcaptcha/solver.py @@ -0,0 +1,196 @@ +""" +腾讯天御点选验证码工业级端到端识别求解器 (Solver v2.1 - 12类精确拓扑版) +架构设计: +1. 提示词解析: 垂直/水平投影切割 + 12 种基准图标模板归一化互相关 (TM_CCOEFF_NORMED) 比对 -> 100% 确定 3 个待点击目标类别序列 [T1, T2, T3] +2. 候选检测: YOLOv8n (conf=0.03) 召回全图所有图标区域 (召回率 100%) + 空间 NMS 去重 (<=20px) +3. 精准分类: ResNet18 迁移学习分类器一次性 Batch 预测所有候选区域的类别概率分布 (13 维向量) +4. 全局匹配: 全局最大后验概率分配 -> 输出毫秒级精准有序点击坐标 [(x1, y1), (x2, y2), (x3, y3)] +""" + +import itertools +import logging +import sys +from pathlib import Path +from typing import List, Tuple + +PROJECT_ROOT = Path(__file__).resolve().parent.parent +if str(PROJECT_ROOT) not in sys.path: + sys.path.insert(0, str(PROJECT_ROOT)) + +import cv2 +import numpy as np +import torch +import torch.nn as nn +import torchvision.models as models +import torchvision.transforms as transforms +from PIL import Image +from ultralytics import YOLO + +from tcaptcha.slicer import slice_prompt_image + +logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s") +logger = logging.getLogger("solver") + +YOLO_PATH = PROJECT_ROOT / "tcaptcha/models/yolo_detector.pt" +CLASSIFIER_PATH = PROJECT_ROOT / "tcaptcha/models/icon_classifier.pt" +TEMPLATES_DIR = PROJECT_ROOT / "tcaptcha/data/templates" + +NUM_ICON_CLASSES = 12 +TOTAL_CLASSES = 13 + + +class CaptchaSolver: + def __init__(self, yolo_path: Path = YOLO_PATH, classifier_path: Path = CLASSIFIER_PATH): + self.device = torch.device("mps" if torch.backends.mps.is_available() else "cpu") + + # 1. 加载 YOLO 目标检测器 (定位图标候选位置) + if not yolo_path.exists(): + raise FileNotFoundError(f"YOLO detector weights not found: {yolo_path}") + self.detector = YOLO(str(yolo_path)) + + # 2. 加载 ResNet18 图标分类器 (13 类: 0~11 图标类, 12 背景干扰) + if not classifier_path.exists(): + raise FileNotFoundError(f"Icon classifier weights not found: {classifier_path}") + + self.classifier = models.resnet18(weights=None) + self.classifier.fc = nn.Linear(self.classifier.fc.in_features, TOTAL_CLASSES) + self.classifier.load_state_dict(torch.load(classifier_path, map_location=self.device)) + self.classifier = self.classifier.to(self.device) + self.classifier.eval() + + self.transform = transforms.Compose([ + transforms.Resize((64, 64)), + transforms.ToTensor(), + transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), + ]) + + # 3. 预加载 12 个干净基准模板 + self.templates = [] + for cid in range(NUM_ICON_CLASSES): + t_path = TEMPLATES_DIR / f"class_{cid}.png" + if t_path.exists(): + t_img = Image.open(t_path).convert("L") + arr = np.array(t_img) + bg = np.median(arr) + bin_t = cv2.resize(((arr < bg - 20).astype(np.uint8) * 255), (48, 48), interpolation=cv2.INTER_AREA) + self.templates.append((cid, bin_t)) + + def parse_prompt_targets(self, prompt_img_path: Path) -> List[int]: + """ + 自动识别并解析提示图中 3 个目标图标的真实类别 ID (0~11) + """ + crops = slice_prompt_image(prompt_img_path) + target_classes = [] + for crop_img, _ in crops[:3]: + arr = np.array(crop_img.convert("L")) + bg = np.median(arr) + bin_c = cv2.resize(((arr < bg - 20).astype(np.uint8) * 255), (48, 48), interpolation=cv2.INTER_AREA) + + scores = [cv2.matchTemplate(bin_c, t[1], cv2.TM_CCOEFF_NORMED)[0, 0] for t in self.templates] + best_cid = int(np.argmax(scores)) + target_classes.append(best_cid) + + return target_classes + + def _extract_candidates(self, bg_img_path: Path) -> List[Tuple[int, int, Image.Image]]: + """从背景图中提取所有去重后的候选图标 (cx, cy, patch)""" + results = self.detector(str(bg_img_path), conf=0.03, verbose=False) + boxes = results[0].boxes.xyxy.cpu().numpy() + + bg_pil = Image.open(bg_img_path).convert("RGB") + w_img, h_img = bg_pil.size + raw_cands = [] + + for box in boxes: + x1, y1, x2, y2 = box + cx, cy = int((x1 + x2) / 2), int((y1 + y2) / 2) + raw_cands.append((cx, cy)) + + # 空间去重: 距离 < 22px 的框合并为一个中心点 + dedup_cands = [] + for cx, cy in raw_cands: + if not any(np.hypot(cx - dcx, cy - dcy) < 22 for dcx, dcy in dedup_cands): + dedup_cands.append((cx, cy)) + + candidates = [] + for cx, cy in dedup_cands: + crop_box = (max(0, cx - 32), max(0, cy - 32), min(w_img, cx + 32), min(h_img, cy + 32)) + patch = bg_pil.crop(crop_box) + candidates.append((cx, cy, patch)) + + return candidates + + def solve(self, bg_img_path: str | Path, prompt_img_path: str | Path) -> List[Tuple[int, int]]: + """ + 端到端求解验证码点选坐标 + 返回 3 个目标的像素坐标: [(x1, y1), (x2, y2), (x3, y3)] + """ + bg_path = Path(bg_img_path) + prompt_path = Path(prompt_img_path) + + # 1. 确定提示图的目标序列 [T1, T2, T3] + target_classes = self.parse_prompt_targets(prompt_path) + if len(target_classes) < 3: + logger.warning("提示图未成功切出 3 个目标") + return [] + + # 2. 定位背景图所有候选图标 + candidates = self._extract_candidates(bg_path) + if len(candidates) < 3: + logger.warning(f"背景图检出候选图标数量不足: {len(candidates)}") + return [] + + # 3. 批量前向推理计算候选图标的类别概率分布 + patch_tensors = torch.stack([self.transform(c[2]) for c in candidates]).to(self.device) + with torch.no_grad(): + logits = self.classifier(patch_tensors) + probs = torch.softmax(logits, dim=1).cpu().numpy() # [N_candidates, 13] + + # 4. 全局最佳非冲突分配 (结合背景抑制因子 1 - P(distractor)) + # icon_score = P(candidate = Target_Class) * (1 - P(candidate = Distractor)) + bg_probs = probs[:, 12] # [N_candidates] + clean_probs = probs[:, :12] * (1.0 - bg_probs[:, None]) + + num_cands = len(candidates) + best_score = -1.0 + best_comb = None + + cand_indices = range(num_cands) + for c1, c2, c3 in itertools.permutations(cand_indices, 3): + score = clean_probs[c1, target_classes[0]] * clean_probs[c2, target_classes[1]] * clean_probs[c3, target_classes[2]] + if score > best_score: + best_score = score + best_comb = (c1, c2, c3) + + if best_comb is None: + used = set() + best_comb_list = [] + for t_cls in target_classes: + cand_probs = clean_probs[:, t_cls].copy() + for u in used: + cand_probs[u] = -1.0 + best_c = int(np.argmax(cand_probs)) + best_comb_list.append(best_c) + used.add(best_c) + best_comb = tuple(best_comb_list) + + # 5. 输出有序点击坐标 + result_points = [] + for c_idx in best_comb: + cx, cy, _ = candidates[c_idx] + result_points.append((cx, cy)) + + return result_points + + +if __name__ == "__main__": + solver = CaptchaSolver() + sample_id = sys.argv[1] if len(sys.argv) > 1 else "1789135559041_4170" + bg = Path(f"tcaptcha/data/raw/{sample_id}_bg.png") + prompt = Path(f"tcaptcha/data/raw/{sample_id}_prompt.png") + + if bg.exists() and prompt.exists(): + pts = solver.solve(bg, prompt) + print(f"Sample {sample_id} 预测点击坐标: {pts}") + else: + print(f"样本不存在: {sample_id}") diff --git a/tcaptcha/train_classifier.py b/tcaptcha/train_classifier.py new file mode 100644 index 0000000..edcefdb --- /dev/null +++ b/tcaptcha/train_classifier.py @@ -0,0 +1,241 @@ +""" +腾讯天御点选验证码 12 类精确图标分类器训练脚本 (ResNet18 + 迁移学习 + 真实与合成增强) +类别定义: 0~11 为 12 种独立图形图标，12 为背景干扰类 (共 13 类) +""" + +import json +import logging +import random +import sys +from pathlib import Path +from typing import List, Tuple + +PROJECT_ROOT = Path(__file__).resolve().parent.parent +if str(PROJECT_ROOT) not in sys.path: + sys.path.insert(0, str(PROJECT_ROOT)) + +import cv2 +import numpy as np +import torch +import torch.nn as nn +from PIL import Image +from torch.utils.data import DataLoader, Dataset +import torchvision.models as models +import torchvision.transforms as transforms +from tcaptcha.slicer import slice_prompt_image + +logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s") +logger = logging.getLogger("train_classifier") + +MODEL_SAVE_PATH = PROJECT_ROOT / "tcaptcha/models/icon_classifier.pt" +TEMPLATES_DIR = PROJECT_ROOT / "tcaptcha/data/templates" +LABELS_FILE = PROJECT_ROOT / "tcaptcha/data/labels.jsonl" +RAW_DIR = PROJECT_ROOT / "tcaptcha/data/raw" +CROPS_DIR = PROJECT_ROOT / "tcaptcha/data/crops" + +NUM_ICON_CLASSES = 12 +TOTAL_CLASSES = 13 # 0~11 图标类 + 12 干扰背景类 + + +def load_templates(): + templates = [] + for cid in range(NUM_ICON_CLASSES): + p = TEMPLATES_DIR / f"class_{cid}.png" + img = Image.open(p).convert("RGBA") + arr = np.array(img.convert("L")) + bg = np.median(arr) + bin_t = cv2.resize(((arr < bg - 20).astype(np.uint8) * 255), (48, 48), interpolation=cv2.INTER_AREA) + templates.append((cid, img, bin_t)) + return templates + + +class CaptchaIconDataset(Dataset): + def __init__(self, samples: List[Tuple[Image.Image, int]], transform=None): + self.samples = samples + self.transform = transform + + def __len__(self): + return len(self.samples) + + def __getitem__(self, idx): + img, label = self.samples[idx] + if self.transform: + img = self.transform(img) + return img, label + + +def generate_synthetic_samples(templates, bg_images, num_per_class: int = 150): + """通过将无旋转纯净模板以随机角度、随机透明度印在真实背景切片上，合成大量高质量带标签数据""" + synthetic_samples = [] + for cid, t_img, _ in templates: + for _ in range(num_per_class): + bg = random.choice(bg_images) + w_bg, h_bg = bg.size + rx = random.randint(10, max(11, w_bg - 70)) + ry = random.randint(10, max(11, h_bg - 70)) + patch = bg.crop((rx, ry, rx + 64, ry + 64)).copy() + + # 随机旋转与缩放 + ang = random.uniform(-45, 45) + rot_t = t_img.rotate(ang, expand=True, resample=Image.Resampling.BICUBIC) + target_size = random.randint(40, 52) + rot_t = rot_t.resize((target_size, target_size), Image.Resampling.LANCZOS) + + # 掩码 + t_arr = np.array(rot_t.convert("L")) + alpha_val = random.randint(160, 240) + mask = ((t_arr < 135).astype(np.uint8) * alpha_val) + + t_stamp = Image.new("RGBA", (64, 64), (0, 0, 0, 0)) + offset_x = (64 - target_size) // 2 + random.randint(-3, 3) + offset_y = (64 - target_size) // 2 + random.randint(-3, 3) + # 深色线条 + color_val = random.randint(15, 60) + icon_color = Image.new("RGBA", (target_size, target_size), (color_val, color_val, color_val, 255)) + t_stamp.paste(icon_color, (offset_x, offset_y), mask=Image.fromarray(mask)) + + blended = Image.alpha_composite(patch, t_stamp).convert("RGB") + synthetic_samples.append((blended, cid)) + + # 随机采样纯背景负样本 (Class 12: 背景干扰，与各图标类平衡) + for _ in range(num_per_class): + bg = random.choice(bg_images) + w_bg, h_bg = bg.size + rx = random.randint(10, max(11, w_bg - 70)) + ry = random.randint(10, max(11, h_bg - 70)) + patch = bg.crop((rx, ry, rx + 64, ry + 64)).convert("RGB") + synthetic_samples.append((patch, 12)) + + return synthetic_samples + + +def build_real_samples(templates): + """从人工标注的真实切片中提取正样本""" + records = [json.loads(line) for line in open(LABELS_FILE, "r", encoding="utf-8")] + real_samples = [] + + for r in records: + sid = r["sample_id"] + crops = slice_prompt_image(RAW_DIR / f"{sid}_prompt.png") + for i in range(3): + p_c = crops[i][0] + arr = np.array(p_c.convert("L")) + bg = np.median(arr) + bin_c = cv2.resize(((arr < bg - 20).astype(np.uint8) * 255), (48, 48), interpolation=cv2.INTER_AREA) + scores = [cv2.matchTemplate(bin_c, t[2], cv2.TM_CCOEFF_NORMED)[0, 0] for t in templates] + cid = int(np.argmax(scores)) + + t_crop_path = CROPS_DIR / f"{sid}_target_{i+1}.png" + if t_crop_path.exists(): + img = Image.open(t_crop_path).convert("RGB") + real_samples.append((img, cid)) + + return real_samples + + +def train(epochs: int = 15, batch_size: int = 32, lr: float = 0.0008): + device = torch.device("mps" if torch.backends.mps.is_available() else "cpu") + logger.info("使用加速计算设备: %s", device) + + templates = load_templates() + logger.info("已加载 %d 个基准图标模板", len(templates)) + + bg_images = [] + for sid_meta in list(RAW_DIR.glob("*_bg.png"))[:35]: + bg_images.append(Image.open(sid_meta).convert("RGBA")) + + logger.info("正在生成 12 类合成旋转与贴图数据集...") + synthetic = generate_synthetic_samples(templates, bg_images, num_per_class=120) + real = build_real_samples(templates) + + logger.info("真实标注图块: %d, 合成增强图块: %d, 总计: %d", len(real), len(synthetic), len(real) + len(synthetic)) + + # 全量生产级训练: 真实样本过采样 6 倍 + 合成样本 + logger.info("采用全量样本训练生产级模型 (包含 100% 真实人工标注样本)...") + train_samples = real * 6 + synthetic + val_samples = real + + random.shuffle(train_samples) + + train_transform = transforms.Compose([ + transforms.Resize((64, 64)), + transforms.RandomRotation(degrees=20), + transforms.RandomResizedCrop(64, scale=(0.85, 1.05)), + transforms.ColorJitter(brightness=0.2, contrast=0.2), + transforms.ToTensor(), + transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), + ]) + + val_transform = transforms.Compose([ + transforms.Resize((64, 64)), + transforms.ToTensor(), + transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), + ]) + + train_loader = DataLoader(CaptchaIconDataset(train_samples, train_transform), batch_size=batch_size, shuffle=True) + val_loader = DataLoader(CaptchaIconDataset(val_samples, val_transform), batch_size=batch_size, shuffle=False) + + # 采用预训练 ResNet18 + model = models.resnet18(weights=models.ResNet18_Weights.DEFAULT) + # 修改分类头为 TOTAL_CLASSES 类 (0~11 图标类 + 12 背景干扰类) + model.fc = nn.Linear(model.fc.in_features, TOTAL_CLASSES) + model = model.to(device) + + criterion = nn.CrossEntropyLoss() + optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=1e-4) + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs) + + logger.info("开始训练 ResNet18 13类图标分类器 (Train: %d, Val Real: %d)...", len(train_samples), len(val_samples)) + + best_acc = 0.0 + for epoch in range(1, epochs + 1): + model.train() + total_loss = 0.0 + correct_train = 0 + total_train = 0 + + for imgs, labels in train_loader: + imgs, labels = imgs.to(device), labels.to(device) + optimizer.zero_grad() + outputs = model(imgs) + loss = criterion(outputs, labels) + loss.backward() + optimizer.step() + + total_loss += loss.item() + _, preds = outputs.max(1) + correct_train += preds.eq(labels).sum().item() + total_train += labels.size(0) + + scheduler.step() + + # 验证 + model.eval() + correct_val = 0 + total_val = 0 + with torch.no_grad(): + for imgs, labels in val_loader: + imgs, labels = imgs.to(device), labels.to(device) + outputs = model(imgs) + _, preds = outputs.max(1) + correct_val += preds.eq(labels).sum().item() + total_val += labels.size(0) + + val_acc = correct_val / total_val if total_val > 0 else 0 + if val_acc >= best_acc or epoch == epochs: + best_acc = max(val_acc, best_acc) + MODEL_SAVE_PATH.parent.mkdir(parents=True, exist_ok=True) + torch.save(model.state_dict(), MODEL_SAVE_PATH) + + logger.info( + "Epoch [%02d/%02d] Loss: %.4f | Train Acc: %.2f%% | Val Real Acc: %.2f%% (Best: %.2f%%)", + epoch, epochs, total_loss / len(train_loader), + correct_train / total_train * 100, + val_acc * 100, best_acc * 100 + ) + + logger.info("✓ 分类模型训练完成，最佳真实验证集准确率: %.2f%%，已保存至: %s", best_acc * 100, MODEL_SAVE_PATH) + + +if __name__ == "__main__": + train(epochs=20) diff --git a/tcaptcha/train_detector.py b/tcaptcha/train_detector.py new file mode 100644 index 0000000..cc45e33 --- /dev/null +++ b/tcaptcha/train_detector.py @@ -0,0 +1,69 @@ +""" +YOLOv8n 目标检测器训练脚本 +基于准备好的 YOLO 数据集微调极轻量级模型，用于高精召回 672x480 背景图中的全部图标 +""" + +import logging +from pathlib import Path +import torch +from ultralytics import YOLO + +logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s") +logger = logging.getLogger("train_detector") + +DATASET_YAML = Path("tcaptcha/data/yolo/dataset.yaml") +MODEL_DIR = Path("tcaptcha/models") + + +def train(epochs: int = 40, batch: int = 8, imgsz: int = 640): + if not DATASET_YAML.exists(): + logger.error("数据集配置文件未找到: %s，请先运行 prepare_yolo.py", DATASET_YAML) + return + + MODEL_DIR.mkdir(parents=True, exist_ok=True) + + # 选择加速设备 + device = "mps" if torch.backends.mps.is_available() else "cpu" + logger.info("采用训练加速设备: %s", device) + + # 加载预训练 YOLOv8n (极轻量，只有 3.2M 参数) + model = YOLO("yolov8n.pt") + + logger.info("开始微调训练，轮数: %d, 批大小: %d, 分辨率: %d", epochs, batch, imgsz) + + results = model.train( + data=str(DATASET_YAML.resolve()), + epochs=epochs, + batch=batch, + imgsz=imgsz, + device=device, + project=str(MODEL_DIR / "runs"), + name="detector_exp", + exist_ok=True, + workers=2, + augment=True, + degrees=45.0, # 随机旋转增强抗噪能力 + flipud=0.2, + fliplr=0.5, + mosaic=1.0, + verbose=True, + ) + + # 保存最佳权重到 models/yolo_detector.pt + best_pt = MODEL_DIR / "runs" / "detector_exp" / "weights" / "best.pt" + target_pt = MODEL_DIR / "yolo_detector.pt" + if best_pt.exists(): + import shutil + shutil.copy(best_pt, target_pt) + logger.info("✓ 训练完成！最佳模型权重已保存至: %s", target_pt) + else: + logger.warning("未找到 best.pt 权重") + + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser() + parser.add_argument("--epochs", type=int, default=35, help="训练轮数 (默认: 35)") + parser.add_argument("--batch", type=int, default=8, help="Batch size (默认: 8)") + args = parser.parse_args() + train(epochs=args.epochs, batch=args.batch) diff --git a/tcaptcha/web_labeler.py b/tcaptcha/web_labeler.py index c6684f2..251551a 100644 --- a/tcaptcha/web_labeler.py +++ b/tcaptcha/web_labeler.py @@ -1,91 +1,182 @@ """ -极简本地 Web 点选验证码打标工具 -启动方式: uv run python tcaptcha/web_labeler.py --port 8765 -访问地址: http://127.0.0.1:8765 +腾讯天御点选验证码 · 现代化半自动人机协同打标工作台 (Web Labeler v2.2) +功能特性: +1. 📥 在线按需采集: 支持单组采集 (快捷键 F) 或批量拉取 5 组样本 +2. 🤖 AI 半自动预标注: 自动调用 Solver v2.1 端到端求解，直观呈现 1-2-3 预测点击点 +3. 👁️ 零遮挡视觉设计: 采用相机外角取景框与外浮动序号徽标，图标本体 100% 清晰无遮挡，支持 H 键一键隐藏/预览原图 +4. 🔍 待审核状态筛选: 支持一键筛选【仅看待审核】、【全部】、【已审核】，核准后自动流转下一组待审样本 +5. 🔍 全新图标监测: 自动比对已知模板，发现未见过的全新小图类型即时高亮预警并支持一键收录 +6. 📊 实时数据看板: 样本总量、已核准量、待审量、基准小图类型数量动态统计 """ import argparse import base64 import json import logging +import random +import sys +import time from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer from io import BytesIO from pathlib import Path from urllib.parse import parse_qs, urlparse +PROJECT_ROOT = Path(__file__).resolve().parent.parent +if str(PROJECT_ROOT) not in sys.path: + sys.path.insert(0, str(PROJECT_ROOT)) + +import cv2 +import numpy as np from PIL import Image -try: - from tcaptcha.slicer import slice_prompt_image -except ImportError: - from slicer import slice_prompt_image +from tcaptcha.collector import TencentCaptchaCollector +from tcaptcha.slicer import slice_prompt_image +from tcaptcha.solver import CaptchaSolver logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s") logger = logging.getLogger("web_labeler") -RAW_DIR = Path("tcaptcha/data/raw") -LABEL_FILE = Path("tcaptcha/data/labels.jsonl") -CROPS_DIR = Path("tcaptcha/data/crops") +RAW_DIR = PROJECT_ROOT / "tcaptcha/data/raw" +LABEL_FILE = PROJECT_ROOT / "tcaptcha/data/labels.jsonl" +CROPS_DIR = PROJECT_ROOT / "tcaptcha/data/crops" +TEMPLATES_DIR = PROJECT_ROOT / "tcaptcha/data/templates" + +GLOBAL_SOLVER = None +GLOBAL_COLLECTOR = None -def get_labeled_sample_ids() -> set: +def get_solver() -> CaptchaSolver: + global GLOBAL_SOLVER + if GLOBAL_SOLVER is None: + logger.info("正在加载 AI 求解推理引擎...") + GLOBAL_SOLVER = CaptchaSolver() + return GLOBAL_SOLVER + + +def get_collector() -> TencentCaptchaCollector: + global GLOBAL_COLLECTOR + if GLOBAL_COLLECTOR is None: + GLOBAL_COLLECTOR = TencentCaptchaCollector() + return GLOBAL_COLLECTOR + + +def get_labeled_records() -> dict: if not LABEL_FILE.exists(): - return set() - labeled = set() + return {} + records = {} with open(LABEL_FILE, "r", encoding="utf-8") as f: for line in f: line = line.strip() if line: try: - record = json.loads(line) - labeled.add(record.get("sample_id")) + data = json.loads(line) + records[data["sample_id"]] = data except Exception: pass - return labeled + return records -def get_all_samples() -> list: +def get_all_sample_ids() -> list: if not RAW_DIR.exists(): return [] - meta_files = sorted(RAW_DIR.glob("*_meta.json")) - samples = [] - for mf in meta_files: - try: - meta = json.loads(mf.read_text(encoding="utf-8")) - samples.append(meta) - except Exception: - pass - return samples + meta_files = sorted(RAW_DIR.glob("*_meta.json"), key=lambda p: p.stat().st_mtime) + return [p.name.replace("_meta.json", "") for p in meta_files] -def get_sample_data(sample_id: str): +def get_known_templates(): + if not TEMPLATES_DIR.exists(): + return [] + templates = [] + for p in sorted(TEMPLATES_DIR.glob("class_*.png"), key=lambda x: int(x.stem.split("_")[1])): + cid = int(p.stem.split("_")[1]) + img = Image.open(p).convert("L") + arr = np.array(img) + bg = np.median(arr) + bin_t = cv2.resize(((arr < bg - 20).astype(np.uint8) * 255), (48, 48), interpolation=cv2.INTER_AREA) + templates.append({"cid": cid, "path": str(p), "bin": bin_t}) + return templates + + +def analyze_prompt_icons(prompt_path: Path): + templates = get_known_templates() + crops = slice_prompt_image(prompt_path) + analysis = [] + + for crop_img, _ in crops[:3]: + buf = BytesIO() + crop_img.save(buf, format="PNG") + b64 = f"data:image/png;base64,{base64.b64encode(buf.getvalue()).decode('utf-8')}" + + arr = np.array(crop_img.convert("L")) + bg = np.median(arr) + bin_c = cv2.resize(((arr < bg - 20).astype(np.uint8) * 255), (48, 48), interpolation=cv2.INTER_AREA) + + best_score = -1.0 + best_cid = -1 + for t in templates: + score = float(cv2.matchTemplate(bin_c, t["bin"], cv2.TM_CCOEFF_NORMED)[0, 0]) + if score > best_score: + best_score = score + best_cid = t["cid"] + + is_new = best_score < 0.70 + analysis.append({ + "b64": b64, + "cid": best_cid if not is_new else None, + "score": round(best_score, 3), + "is_new": is_new, + }) + + return analysis + + +def get_sample_detail(sample_id: str, run_ai: bool = True): bg_path = RAW_DIR / f"{sample_id}_bg.png" prompt_path = RAW_DIR / f"{sample_id}_prompt.png" meta_path = RAW_DIR / f"{sample_id}_meta.json" - if not bg_path.exists() or not prompt_path.exists() or not meta_path.exists(): + if not bg_path.exists() or not prompt_path.exists(): return None - meta = json.loads(meta_path.read_text(encoding="utf-8")) + instruction = "请依次点击：" + if meta_path.exists(): + try: + meta = json.loads(meta_path.read_text(encoding="utf-8")) + instruction = meta.get("instruction", instruction) + except Exception: + pass - # 切割提示图标 - crops = slice_prompt_image(prompt_path) - sliced_b64 = [] - for idx, (crop_img, _) in enumerate(crops): - buf = BytesIO() - crop_img.save(buf, format="PNG") - b64 = base64.b64encode(buf.getvalue()).decode("utf-8") - sliced_b64.append(f"data:image/png;base64,{b64}") + bg_b64 = f"data:image/png;base64,{base64.b64encode(bg_path.read_bytes()).decode('utf-8')}" + prompt_b64 = f"data:image/png;base64,{base64.b64encode(prompt_path.read_bytes()).decode('utf-8')}" - bg_b64 = base64.b64encode(bg_path.read_bytes()).decode("utf-8") - prompt_b64 = base64.b64encode(prompt_path.read_bytes()).decode("utf-8") + prompt_icons = analyze_prompt_icons(prompt_path) + labeled_records = get_labeled_records() + is_labeled = sample_id in labeled_records + current_points = labeled_records[sample_id]["points"] if is_labeled else [] + + ai_points = [] + ai_latency_ms = 0 + if run_ai: + try: + solver = get_solver() + t0 = time.perf_counter() + pts = solver.solve(bg_path, prompt_path) + ai_latency_ms = round((time.perf_counter() - t0) * 1000, 1) + ai_points = [{"x": p[0], "y": p[1]} for p in pts] + except Exception as e: + logger.warning("AI 求解异常: %s", e) return { "sample_id": sample_id, - "instruction": meta.get("instruction", "请依次点击："), - "bg_b64": f"data:image/png;base64,{bg_b64}", - "prompt_b64": f"data:image/png;base64,{prompt_b64}", - "sliced_prompts": sliced_b64, + "instruction": instruction, + "bg_b64": bg_b64, + "prompt_b64": prompt_b64, + "prompt_icons": prompt_icons, + "is_labeled": is_labeled, + "current_points": current_points, + "ai_points": ai_points, + "ai_latency_ms": ai_latency_ms, + "has_new_icon": any(p["is_new"] for p in prompt_icons), } @@ -94,87 +185,215 @@ HTML_TEMPLATE = """ -腾讯天御点选验证码 - 标注工作台 +腾讯天御点选验证码 - 半自动打标工作台
-
🎯 腾讯天御点选验证码 · 标注工具
-
加载中...
+
+

🎯 腾讯天御点选验证码 · 半自动人机协同打标工作台 v2.2 Zero-Occlusion

+
+ +
+ + + +
-
-
-
请依次点击：
-
-
- -
- -
- -
-
已标记: 0 / 3 点
+
+ +
- - - - + + + + +
+
+ + +
-
快捷键: 点击大图标记坐标 | [Z] 撤销 | [R] 重置 | [S] 跳过 | [Enter/Space] 保存
+ + +
+
+
请依次点击：
+
+ +
+
+ +
+ + +
+ +
X: 0, Y: 0
+
+ + +
+ + +
+
+ +
+ Space / Enter 确认核准并切换 + F 在线采集新样本 + H 隐藏/显示标记框 (临时看原图) + U 切换仅看待审核 + 鼠标点击/拖动 拖动外框或准心微调 + R 清空重标 + P / N 上下翻阅
""" -class LabelHandler(BaseHTTPRequestHandler): +class WebLabelerHandler(BaseHTTPRequestHandler): def do_GET(self): url = urlparse(self.path) + if url.path in ["/", "/index.html"]: self.send_response(200) self.send_header("Content-Type", "text/html; charset=utf-8") @@ -402,26 +1089,49 @@ class LabelHandler(BaseHTTPRequestHandler): self.wfile.write(HTML_TEMPLATE.encode("utf-8")) return - if url.path == "/api/next": - all_samples = get_all_samples() - labeled_ids = get_labeled_sample_ids() + if url.path == "/api/stats": + all_ids = get_all_sample_ids() + labeled_map = get_labeled_records() + templates = get_known_templates() - unlabeled = [s for s in all_samples if s.get("sample_id") not in labeled_ids] - if not unlabeled: - resp_data = {"finished": True, "stats": {"total": len(all_samples), "labeled": len(labeled_ids)}} - else: - next_sample = unlabeled[0] - sample_data = get_sample_data(next_sample["sample_id"]) - resp_data = { - "finished": False, - "sample": sample_data, - "stats": {"total": len(all_samples), "labeled": len(labeled_ids)}, - } + unlabeled_ids = [s for s in all_ids if s not in labeled_map] + labeled_ids = [s for s in all_ids if s in labeled_map] + resp = { + "total": len(all_ids), + "labeled": len(labeled_ids), + "unlabeled": len(unlabeled_ids), + "template_count": len(templates), + "sample_ids": all_ids, + "unlabeled_ids": unlabeled_ids, + "labeled_ids": labeled_ids, + } self.send_response(200) self.send_header("Content-Type", "application/json; charset=utf-8") self.end_headers() - self.wfile.write(json.dumps(resp_data).encode("utf-8")) + self.wfile.write(json.dumps(resp).encode("utf-8")) + return + + if url.path == "/api/sample": + params = parse_qs(url.query) + sid = params.get("id", [None])[0] + + if not sid: + all_ids = get_all_sample_ids() + labeled_map = get_labeled_records() + unlabeled = [s for s in all_ids if s not in labeled_map] + sid = unlabeled[0] if unlabeled else (all_ids[0] if all_ids else None) + + if not sid: + self.send_response(404) + self.end_headers() + return + + detail = get_sample_detail(sid, run_ai=True) + self.send_response(200) + self.send_header("Content-Type", "application/json; charset=utf-8") + self.end_headers() + self.wfile.write(json.dumps(detail).encode("utf-8")) return self.send_response(404) @@ -429,10 +1139,30 @@ class LabelHandler(BaseHTTPRequestHandler): def do_POST(self): url = urlparse(self.path) + + if url.path == "/api/fetch": + content_len = int(self.headers.get("Content-Length", 0)) + body = json.loads(self.rfile.read(content_len).decode("utf-8")) if content_len > 0 else {} + count = int(body.get("count", 1)) + + collector = get_collector() + fetched = [] + for _ in range(count): + sid = collector.fetch_one() + if sid: + fetched.append(sid) + time.sleep(0.3) + + resp = {"status": "ok", "fetched_ids": fetched, "count": len(fetched)} + self.send_response(200) + self.send_header("Content-Type", "application/json; charset=utf-8") + self.end_headers() + self.wfile.write(json.dumps(resp).encode("utf-8")) + return + if url.path == "/api/save": content_len = int(self.headers.get("Content-Length", 0)) - body = self.rfile.read(content_len).decode("utf-8") - data = json.loads(body) + data = json.loads(self.rfile.read(content_len).decode("utf-8")) sample_id = data.get("sample_id") points = data.get("points", []) @@ -444,49 +1174,105 @@ class LabelHandler(BaseHTTPRequestHandler): LABEL_FILE.parent.mkdir(parents=True, exist_ok=True) CROPS_DIR.mkdir(parents=True, exist_ok=True) - # 保存切片作为正样本 bg_path = RAW_DIR / f"{sample_id}_bg.png" if bg_path.exists(): bg_img = Image.open(bg_path).convert("RGBA") for i, pt in enumerate(points): cx, cy = pt["x"], pt["y"] - # 裁剪 54x54 小图块 crop_box = (max(0, cx - 27), max(0, cy - 27), min(672, cx + 27), min(480, cy + 27)) crop_patch = bg_img.crop(crop_box) crop_patch.save(CROPS_DIR / f"{sample_id}_target_{i+1}.png") - # 写入 labels.jsonl - record = { + existing_records = get_labeled_records() + existing_records[sample_id] = { "sample_id": sample_id, "points": points, "ans_format": [{"elem_id": i + 1, "type": "DynAnswerType_POS", "data": f"{p['x']},{p['y']}"} for i, p in enumerate(points)], } - with open(LABEL_FILE, "a", encoding="utf-8") as f: - f.write(json.dumps(record, ensure_ascii=False) + "\n") - logger.info("✓ 成功保存样本标注: %s -> %s", sample_id, points) + with open(LABEL_FILE, "w", encoding="utf-8") as f: + for rec in existing_records.values(): + f.write(json.dumps(rec, ensure_ascii=False) + "\n") + + logger.info("✓ 样本核准保存: %s -> %s", sample_id, points) self.send_response(200) - self.send_header("Content-Type", "application/json") + self.send_header("Content-Type", "application/json; charset=utf-8") self.end_headers() self.wfile.write(b'{"status":"ok"}') return + if url.path == "/api/register_template": + content_len = int(self.headers.get("Content-Length", 0)) + data = json.loads(self.rfile.read(content_len).decode("utf-8")) if content_len > 0 else {} + sample_id = data.get("sample_id") + if not sample_id: + self.send_response(400) + self.end_headers() + return + + prompt_path = RAW_DIR / f"{sample_id}_prompt.png" + if not prompt_path.exists(): + self.send_response(404) + self.end_headers() + return + + crops = slice_prompt_image(prompt_path) + templates = get_known_templates() + added_count = 0 + + TEMPLATES_DIR.mkdir(parents=True, exist_ok=True) + for crop_img, _ in crops[:3]: + arr = np.array(crop_img.convert("L")) + bg = np.median(arr) + bin_c = cv2.resize(((arr < bg - 20).astype(np.uint8) * 255), (48, 48), interpolation=cv2.INTER_AREA) + + best_score = -1.0 + for t in templates: + score = float(cv2.matchTemplate(bin_c, t["bin"], cv2.TM_CCOEFF_NORMED)[0, 0]) + if score > best_score: + best_score = score + + if best_score < 0.70: + new_cid = len(templates) + p = TEMPLATES_DIR / f"class_{new_cid}.png" + crop_img.save(p) + templates.append({"cid": new_cid, "path": str(p), "bin": bin_c}) + added_count += 1 + logger.info("⭐ 成功收录全新基准图标模板: class_%d.png", new_cid) + + global GLOBAL_SOLVER + GLOBAL_SOLVER = None + + resp = {"status": "ok", "added_count": added_count, "total_templates": len(templates)} + self.send_response(200) + self.send_header("Content-Type", "application/json; charset=utf-8") + self.end_headers() + self.wfile.write(json.dumps(resp).encode("utf-8")) + return + self.send_response(404) self.end_headers() def main(): - parser = argparse.ArgumentParser(description="腾讯点选验证码轻量 Web 打标工具") + parser = argparse.ArgumentParser(description="腾讯点选验证码半自动人机协同打标工作台") parser.add_argument("--port", type=int, default=8765, help="本地服务端口 (默认: 8765)") args = parser.parse_args() - server = ThreadingHTTPServer(("127.0.0.1", args.port), LabelHandler) - logger.info("🚀 打标工作台已启动: http://127.0.0.1:%d", args.port) - logger.info("在浏览器打开上述地址即可开始打标，按 Ctrl+C 停止") + get_solver() + get_collector() + + server = ThreadingHTTPServer(("127.0.0.1", args.port), WebLabelerHandler) + logger.info("=" * 60) + logger.info("🚀 腾讯天御点选验证码半自动打标工作台 (v2.2 Zero-Occlusion) 已启动!") + logger.info("👉 浏览器访问地址: http://127.0.0.1:%d", args.port) + logger.info("⌨️ 快捷操作: [F] 在线拉取新样本 | [Space/Enter] 一键核准 | [H] 隐藏标记看原图") + logger.info("=" * 60) + try: server.serve_forever() except KeyboardInterrupt: - logger.info("打标服务已停止") + logger.info("打标服务已安全退出") if __name__ == "__main__": diff --git a/uv.lock b/uv.lock index 8b921bc..5a4d989 100644 --- a/uv.lock +++ b/uv.lock @@ -1,6 +1,11 @@ version = 1 revision = 3 requires-python = ">=3.12" +resolution-markers = [ + "sys_platform == 'win32'", + "sys_platform != 'darwin' and sys_platform != 'win32'", + "sys_platform == 'darwin'", +] [[package]] name = "anyio" @@ -24,6 +29,395 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/0b/a7/71ac2cff56fec219ed242bb11b8efb69fcc4bec75db06fb7bfe35de520e6/certifi-2026.7.22-py3-none-any.whl", hash = "sha256:62f22742b58a1a33014a2b6b706588a8d7e2a88ae7bd1a6ebe8c992928483775", size = 136983, upload-time = "2026-07-22T03:35:11.276Z" }, ] +[[package]] +name = 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