# -*- coding: utf-8 -*- """ 腾讯天御点选验证码工业级纯 ONNX 视觉求解器 (Solver v3.0 - Pure ONNX Edition) 特性: 1. 零 PyTorch / 零 Ultralytics 依赖：采用 onnxruntime 推理引擎 (从 1.5GB 骤降至 15MB) 2. 提示词解析: 垂直/水平投影切片 + ResNet18 ONNX 分类器直接预测目标序列 3. 候选图标定位: YOLOv8n ONNX + Letterbox 自适应缩放 + OpenCV cv2.dnn.NMSBoxes 精准去重 4. 全局匹配: 后验概率分布优化 + 背景干扰项抑制因子 + 全局非冲突组合搜索，输出有序像素点击坐标 """ import io import itertools import logging import sys from pathlib import Path from typing import List, Tuple, Union CURRENT_DIR = Path(__file__).resolve().parent PROJECT_ROOT = CURRENT_DIR.parent if str(PROJECT_ROOT) not in sys.path: sys.path.insert(0, str(PROJECT_ROOT)) if str(CURRENT_DIR) not in sys.path: sys.path.insert(0, str(CURRENT_DIR)) import cv2 import numpy as np import onnxruntime as ort from PIL import Image try: from slicer import slice_prompt_image except ImportError: from tcaptcha.slicer import slice_prompt_image logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s") logger = logging.getLogger("solver") # 权重路径解析 (优先 models/ 目录，兼容当前目录) YOLO_ONNX_PATH = CURRENT_DIR / "models/yolo_detector.onnx" if not YOLO_ONNX_PATH.exists(): YOLO_ONNX_PATH = CURRENT_DIR / "yolo_detector.onnx" CLASSIFIER_ONNX_PATH = CURRENT_DIR / "models/icon_classifier.onnx" if not CLASSIFIER_ONNX_PATH.exists(): CLASSIFIER_ONNX_PATH = CURRENT_DIR / "icon_classifier.onnx" class CaptchaSolver: def __init__(self, yolo_path: Path = YOLO_ONNX_PATH, classifier_path: Path = CLASSIFIER_ONNX_PATH): if not yolo_path.exists(): raise FileNotFoundError(f"YOLO ONNX 权重文件不存在: {yolo_path}") if not classifier_path.exists(): raise FileNotFoundError(f"Classifier ONNX 权重文件不存在: {classifier_path}") # 配置轻量级 CPU 推理会话 sess_options = ort.SessionOptions() sess_options.inter_op_num_threads = 2 sess_options.intra_op_num_threads = 2 sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL self.yolo_sess = ort.InferenceSession(str(yolo_path), sess_options, providers=["CPUExecutionProvider"]) self.cls_sess = ort.InferenceSession(str(classifier_path), sess_options, providers=["CPUExecutionProvider"]) @staticmethod def _preprocess_patch(img: Image.Image) -> np.ndarray: """对小图标图像进行 64x64 归一化预处理 (匹配 ResNet18 输入标准)""" resized = img.convert("RGB").resize((64, 64), Image.Resampling.BILINEAR) arr = np.array(resized, dtype=np.float32) / 255.0 mean = np.array([0.485, 0.456, 0.406], dtype=np.float32) std = np.array([0.229, 0.224, 0.225], dtype=np.float32) norm = (arr - mean) / std return np.transpose(norm, (2, 0, 1)) # (3, 64, 64) def parse_prompt_targets(self, prompt_img: Union[str, Path, bytes, Image.Image]) -> List[int]: """自动切片并解析提示图中 3 个目标图标的类别 ID (0~11)""" crops = slice_prompt_image(prompt_img) if not crops: return [] patch_blobs = np.stack([self._preprocess_patch(c[0]) for c in crops[:3]], axis=0).astype(np.float32) logits = self.cls_sess.run(None, {"input": patch_blobs})[0] # shape: (N, 13) return [int(np.argmax(l[:12])) for l in logits] def _extract_candidates(self, bg_img: Union[str, Path, bytes, Image.Image]) -> List[Tuple[int, int, Image.Image]]: """利用 YOLOv8n ONNX 召回背景图所有图标候选位置并去重""" if isinstance(bg_img, Image.Image): bg_pil = bg_img.convert("RGB") elif isinstance(bg_img, bytes): bg_pil = Image.open(io.BytesIO(bg_img)).convert("RGB") else: bg_pil = Image.open(bg_img).convert("RGB") orig_w, orig_h = bg_pil.size img_np = np.array(bg_pil) # 1. Letterbox 保持宽高比填充缩放至 640x640 r = min(640.0 / orig_h, 640.0 / orig_w) new_w, new_h = int(round(orig_w * r)), int(round(orig_h * r)) resized = cv2.resize(img_np, (new_w, new_h), interpolation=cv2.INTER_LINEAR) dw = (640.0 - new_w) / 2.0 dh = (640.0 - new_h) / 2.0 top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) padded = cv2.copyMakeBorder(resized, top, bottom, left, right, cv2.BORDER_CONSTANT, value=(114, 114, 114)) blob = padded.astype(np.float32) / 255.0 blob = np.transpose(blob, (2, 0, 1))[None, ...] # 2. ONNX 目标检测前向推理 yolo_out = self.yolo_sess.run(None, {"images": blob})[0][0].T # (8400, 5) valid = yolo_out[yolo_out[:, 4] >= 0.03] boxes_xywh = [] scores = [] for row in valid: cx, cy, w, h, conf = row x1 = (cx - w / 2.0 - dw) / r y1 = (cy - h / 2.0 - dh) / r boxes_xywh.append([int(x1), int(y1), int(w / r), int(h / r)]) scores.append(float(conf)) # 3. OpenCV NMS 筛选重叠边界框 indices = cv2.dnn.NMSBoxes(boxes_xywh, scores, score_threshold=0.03, nms_threshold=0.45) raw_cands = [] if len(indices) > 0: for idx in indices: x, y, w, h = boxes_xywh[idx] raw_cands.append((int(x + w / 2.0), int(y + h / 2.0))) # 4. 空间中心点近邻聚类去重 (<=22px) 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(orig_w, cx + 32), min(orig_h, cy + 32)) patch = bg_pil.crop(crop_box) candidates.append((cx, cy, patch)) return candidates def solve( self, bg_img: Union[str, Path, bytes, Image.Image], prompt_img: Union[str, Path, bytes, Image.Image], ) -> List[Tuple[int, int]]: """ 端到端纯 ONNX 求解验证码点选坐标 返回 3 个目标的像素坐标序列: [(x1, y1), (x2, y2), (x3, y3)] """ # 1. 确定提示图的目标序列 [T1, T2, T3] target_classes = self.parse_prompt_targets(prompt_img) 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_img) if len(candidates) < 3: logger.warning(f"背景图检出候选图标数量不足: {len(candidates)}") return [] # 3. 批量推理计算候选图标的类别概率分布 (13 维) patch_blobs = np.stack([self._preprocess_patch(c[2]) for c in candidates], axis=0).astype(np.float32) logits = self.cls_sess.run(None, {"input": patch_blobs})[0] exp = np.exp(logits - np.max(logits, axis=1, keepdims=True)) probs = exp / np.sum(exp, axis=1, keepdims=True) # 4. 全局后验概率优化 (结合背景干扰项抑制因子 1 - P(distractor)) bg_probs = probs[:, 12] 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() 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)) comb_list.append(best_c) used.add(best_c) best_comb = tuple(comb_list) # 5. 输出有序点击坐标 return [(candidates[idx][0], candidates[idx][1]) for idx in best_comb] if __name__ == "__main__": solver = CaptchaSolver() print("✓ CaptchaSolver ONNX 引擎初始化成功！")