PaddleX/zhch/adapters/doc_preprocessor_adapter.py

"""
文档预处理适配器
使用 MinerU 的方向判断算法，但保留 PaddleX 的模型
"""

import sys
from pathlib import Path
from typing import Any, Dict, List, Optional, Union, Tuple
import numpy as np
import cv2

from paddlex.inference.pipelines.doc_preprocessor.result import DocPreprocessorResult
from paddlex.inference.common.reader import ReadImage
from paddlex.inference.common.batch_sampler import ImageBatchSampler
from paddlex.inference.pipelines.components import rotate_image


class EnhancedDocPreprocessor:
    """
    增强版文档预处理器
    核心思路：采用 MinerU 的两阶段方向判断算法
    1. 快速过滤：宽高比判断（纵向图片才需要方向分类）
    2. OCR 引导：检测文本框，判断是否有大量垂直文本
    3. 精确分类：仅对疑似旋转的图片调用分类模型
    """
    
    def __init__(
        self,
        doc_ori_classify_model,
        doc_unwarping_model,
        ocr_det_model=None,  # 🎯 OCR 检测模型（可选）
        device: str = "cpu",
        use_doc_orientation_classify: bool = True,
        use_doc_unwarping: bool = False,
        batch_size: int = 1,
    ):
        """
        Args:
            doc_ori_classify_model: PaddleX 的方向分类模型
            doc_unwarping_model: PaddleX 的文档矫正模型
            ocr_det_model: OCR 文本检测模型（用于判断是否需要旋转，可选）
            device: 设备类型（cpu/gpu）
            use_doc_orientation_classify: 是否使用方向分类
            use_doc_unwarping: 是否使用文档矫正
            batch_size: 批处理大小
        """
        self.doc_ori_classify_model = doc_ori_classify_model
        self.doc_unwarping_model = doc_unwarping_model
        self.device = device
        self.use_doc_orientation_classify = use_doc_orientation_classify
        self.use_doc_unwarping = use_doc_unwarping
        self.batch_size = batch_size
        
        self.img_reader = ReadImage(format="BGR")
        self.batch_sampler = ImageBatchSampler(batch_size=batch_size)
        
        # 🎯 MinerU 算法参数
        self.portrait_threshold = 1.2  # 宽高比阈值
        self.vertical_ratio_threshold = 0.28  # 垂直文本框比例阈值
        self.min_vertical_count = 3  # 最少垂直文本框数量
        
        # 🎯 初始化 OCR 检测模型（只初始化一次）
        self.ocr_det_model = ocr_det_model
        if self.ocr_det_model is None:
            self._initialize_ocr_det_model()
        
        print(f"📐 Enhanced DocPreprocessor initialized")
        print(f"   - Device: {self.device}")
        print(f"   - Portrait threshold: {self.portrait_threshold}")
        print(f"   - Vertical ratio threshold: {self.vertical_ratio_threshold}")
        print(f"   - Min vertical count: {self.min_vertical_count}")
        print(f"   - OCR detection model: {'✅ Available' if self.ocr_det_model else '❌ Not available'}")
    
    def _initialize_ocr_det_model(self):
        """初始化 OCR 检测模型（只执行一次）"""
        try:
            from paddlex import create_model
            
            print("🔧 Initializing OCR detection model...")
            self.ocr_det_model = create_model(
                'PP-OCRv5_server_det',
                device=self.device
            )
            print("✅ OCR detection model initialized successfully")
            
        except Exception as e:
            print(f"⚠️  Failed to initialize OCR detection model: {e}")
            print("   Will skip OCR-guided filtering")
            self.ocr_det_model = None
    
    def _is_portrait_image(self, image: np.ndarray) -> bool:
        """判断是否为纵向图片"""
        img_height, img_width = image.shape[:2]
        aspect_ratio = img_height / img_width if img_width > 0 else 1.0
        is_portrait = aspect_ratio > self.portrait_threshold
        print(f"   📏 Image size: {img_width}x{img_height}, aspect_ratio: {aspect_ratio:.2f}, is_portrait: {is_portrait}")
        return is_portrait
    
    def _detect_vertical_text_boxes(self, image: np.ndarray) -> Tuple[int, int]:
        """
        检测图片中的垂直文本框
        
        Returns:
            (vertical_count, total_count): 垂直文本框数量和总数量
        """
        if self.ocr_det_model is None:
            print("   ⚠️  OCR detection model not available")
            return 0, 0
        
        try:
            # 🎯 调用 OCR 检测模型
            det_results = list(self.ocr_det_model([image]))
            if not det_results or len(det_results) == 0:
                print("   ℹ️  No OCR detection results")
                return 0, 0
            
            det_result = det_results[0]
            
            # 🎯 从检测结果中提取文本框
            # PaddleX 的检测结果格式: {"dt_polys": [...], ...}
            boxes = None
            if isinstance(det_result, dict):
                boxes = det_result.get('dt_polys', None)
            elif isinstance(det_result, np.ndarray):
                boxes = det_result
            
            if boxes is None or len(boxes) == 0:
                print("   ℹ️  No text boxes detected")
                return 0, 0
            
            # 🎯 统计垂直文本框
            vertical_count = 0
            total_count = len(boxes)
            
            # 🎯 处理 numpy 数组格式: shape=(N, 4, 2)
            if isinstance(boxes, np.ndarray) and len(boxes.shape) == 3 and boxes.shape[1] == 4 and boxes.shape[2] == 2:
                # 格式: (N, 4, 2) - 每个框有4个点，每个点有(x,y)坐标
                for box in boxes:
                    # box: shape=(4, 2) - [[x1,y1], [x2,y2], [x3,y3], [x4,y4]]
                    p1, p2, p3, p4 = box
                    
                    # 计算宽高
                    width = abs(float(p2[0] - p1[0]))  # x2 - x1
                    height = abs(float(p3[1] - p2[1]))  # y3 - y2
                    
                    if height == 0:
                        continue
                    
                    aspect_ratio = width / height
                    
                    # 🎯 MinerU 的判断标准：宽高比 < 0.8 为垂直文本
                    if aspect_ratio < 0.8:
                        vertical_count += 1
            
            print(f"   📊 OCR detection: {vertical_count}/{total_count} vertical boxes ({vertical_count/total_count:.1%} vertical)")
            return vertical_count, total_count
            
        except Exception as e:
            print(f"   ⚠️  OCR detection failed: {e}")
            import traceback
            traceback.print_exc()
            return 0, 0
    
    def _should_classify_orientation(self, image: np.ndarray) -> bool:
        """
        判断是否需要进行方向分类
        参考 MinerU 的两阶段判断逻辑
        
        Returns:
            True: 需要分类
            False: 跳过分类（直接使用原图）
        """
        print("🔍 Checking if orientation classification is needed...")
        
        # 🎯 阶段 1: 快速过滤 - 宽高比检查
        if not self._is_portrait_image(image):
            print("   ⏭️  Skipped: Image is landscape")
            return False
        
        # 🎯 阶段 2: OCR 引导判断 - 检测垂直文本框
        vertical_count, total_count = self._detect_vertical_text_boxes(image)
        
        if total_count == 0:
            print("   ⏭️  Skipped: No text detected")
            return False
        
        # 🎯 MinerU 的判断标准：
        # 垂直文本框比例 >= 28% 且数量 >= 3，才认为可能需要旋转
        vertical_ratio = vertical_count / total_count
        is_rotated = (
            vertical_ratio >= self.vertical_ratio_threshold and 
            vertical_count >= self.min_vertical_count
        )
        
        print(f"   📈 Vertical ratio: {vertical_ratio:.1%} (threshold: {self.vertical_ratio_threshold:.1%})")
        print(f"   📊 Vertical count: {vertical_count} (min: {self.min_vertical_count})")
        print(f"   🎯 Need classification: {is_rotated}")
        
        return is_rotated
    
    def _predict_orientation(self, image: np.ndarray) -> int:
        """
        预测图像方向
        
        Args:
            image: BGR 格式的图像
            
        Returns:
            旋转角度 (0, 90, 180, 270)
        """
        if not self.use_doc_orientation_classify or self.doc_ori_classify_model is None:
            return 0
        
        try:
            # 调用 PaddleX 的分类模型
            preds = list(self.doc_ori_classify_model([image]))
            if preds and len(preds) > 0:
                pred = preds[0]
                angle = int(pred["label_names"][0])
                print(f"   🔄 Orientation classification result: {angle}°")
                return angle
            return 0
        except Exception as e:
            print(f"   ⚠️  Orientation prediction failed: {e}")
            return 0
    
    def predict(
        self,
        input: Union[str, List[str], np.ndarray, List[np.ndarray]],
        use_doc_orientation_classify: Optional[bool] = None,
        use_doc_unwarping: Optional[bool] = None,
    ):
        """
        预测文档预处理结果
        
        Args:
            input: 输入图像路径、数组或列表
            use_doc_orientation_classify: 是否使用方向分类
            use_doc_unwarping: 是否使用文档矫正
            
        Yields:
            DocPreprocessorResult: 预处理结果
        """
        # 处理模型设置
        if use_doc_orientation_classify is None:
            use_doc_orientation_classify = self.use_doc_orientation_classify
        if use_doc_unwarping is None:
            use_doc_unwarping = self.use_doc_unwarping
        
        model_settings = {
            "use_doc_orientation_classify": use_doc_orientation_classify,
            "use_doc_unwarping": use_doc_unwarping,
        }
        
        print(f"\n{'='*60}")
        print(f"🎯 Enhanced DocPreprocessor - MinerU Algorithm")
        print(f"   Settings: orientation={use_doc_orientation_classify}, unwarping={use_doc_unwarping}")
        print(f"{'='*60}\n")
        
        # 批处理
        for batch_data in self.batch_sampler(input):
            # 读取图像
            image_arrays = self.img_reader(batch_data.instances)
            
            # 🎯 增强的方向分类和旋转逻辑
            angles = []
            rot_imgs = []
            
            for idx, img in enumerate(image_arrays):
                print(f"\n📄 Processing image {idx + 1}/{len(image_arrays)}")
                
                if use_doc_orientation_classify:
                    # 🎯 关键改进：先判断是否需要分类
                    if self._should_classify_orientation(img):
                        # 需要分类：调用模型预测角度
                        angle = self._predict_orientation(img)
                    else:
                        # 跳过分类：直接使用 0 度
                        angle = 0
                        print("   ⏭️  Skipped orientation classification")
                    
                    angles.append(angle)
                    if angle != 0:
                        rot_img = rotate_image(img, angle)
                    else:
                        rot_img = img
                    rot_imgs.append(rot_img)
                else:
                    angles.append(-1)  # -1 表示未进行方向分类
                    rot_imgs.append(img)
            
            # 文档矫正
            if use_doc_unwarping and self.doc_unwarping_model is not None:
                output_imgs = [
                    item["doctr_img"][:, :, ::-1]
                    for item in self.doc_unwarping_model(rot_imgs)
                ]
            else:
                output_imgs = rot_imgs
            
            # 生成结果
            for input_path, page_index, image_array, angle, rot_img, output_img in zip(
                batch_data.input_paths,
                batch_data.page_indexes,
                image_arrays,
                angles,
                rot_imgs,
                output_imgs,
            ):
                single_img_res = {
                    "input_path": input_path,
                    "page_index": page_index,
                    "input_img": image_array,
                    "model_settings": model_settings,
                    "angle": angle,
                    "rot_img": rot_img,
                    "output_img": output_img,
                }
                yield DocPreprocessorResult(single_img_res)
    
    def __call__(self, *args, **kwargs):
        """支持像函数一样调用"""
        return self.predict(*args, **kwargs)


class DocPreprocessorAdapter:
    """
    文档预处理适配器
    替换 _DocPreprocessorPipeline 的 predict 方法
    """
    
    _original_predict = None
    _shared_ocr_det_model = None  # 🎯 共享的 OCR 检测模型
    _enhanced_preprocessor_cache = {}  # 🎯 缓存 enhanced_preprocessor 实例
    
    @classmethod
    def _get_cache_key(cls, device: str, use_doc_orientation_classify: bool, 
                       use_doc_unwarping: bool, batch_size: int) -> str:
        """生成缓存键"""
        return f"{device}_{use_doc_orientation_classify}_{use_doc_unwarping}_{batch_size}"
    
    @classmethod
    def apply(cls, use_enhanced: bool = True):
        """
        应用适配器
        
        Args:
            use_enhanced: 是否使用增强版预处理器
        """
        if not use_enhanced:
            cls.restore()
            return False
        
        try:
            from paddlex.inference.pipelines.doc_preprocessor import pipeline
            
            # 保存原始方法
            if cls._original_predict is None:
                cls._original_predict = pipeline._DocPreprocessorPipeline.predict
            
            # 创建增强版 predict 方法
            def enhanced_predict(
                self,
                input: Union[str, List[str], np.ndarray, List[np.ndarray]],
                use_doc_orientation_classify: Optional[bool] = None,
                use_doc_unwarping: Optional[bool] = None,
            ):
                """增强版 predict 方法"""
                
                # 🎯 关键改进 1：初始化共享的 OCR 检测模型（只初始化一次）
                if cls._shared_ocr_det_model is None:
                    print("\n" + "="*80)
                    print(">>> [Adapter] Enhanced DocPreprocessor - First Time Initialization")
                    print("="*80)
                    print("🔧 Initializing shared OCR detection model...")
                    try:
                        from paddlex import create_model
                        cls._shared_ocr_det_model = create_model(
                            'PP-OCRv5_server_det',
                            device=self.device
                        )
                        print("✅ Shared OCR detection model initialized")
                    except Exception as e:
                        print(f"⚠️  Failed to initialize OCR detection model: {e}")
                        cls._shared_ocr_det_model = None
                
                # 🎯 关键改进 2：使用缓存的 enhanced_preprocessor（只创建一次）
                cache_key = cls._get_cache_key(
                    device=self.device,
                    use_doc_orientation_classify=self.use_doc_orientation_classify,
                    use_doc_unwarping=self.use_doc_unwarping,
                    batch_size=self.batch_sampler.batch_size
                )
                
                if cache_key not in cls._enhanced_preprocessor_cache:
                    print("🔧 Creating new enhanced preprocessor instance...")
                    enhanced_preprocessor = EnhancedDocPreprocessor(
                        doc_ori_classify_model=self.doc_ori_classify_model if self.use_doc_orientation_classify else None,
                        doc_unwarping_model=self.doc_unwarping_model if self.use_doc_unwarping else None,
                        ocr_det_model=cls._shared_ocr_det_model,  # 使用共享的模型
                        device=self.device,
                        use_doc_orientation_classify=self.use_doc_orientation_classify,
                        use_doc_unwarping=self.use_doc_unwarping,
                        batch_size=self.batch_sampler.batch_size,
                    )
                    cls._enhanced_preprocessor_cache[cache_key] = enhanced_preprocessor
                    print(f"✅ Enhanced preprocessor cached with key: {cache_key}")
                else:
                    enhanced_preprocessor = cls._enhanced_preprocessor_cache[cache_key]
                    print(f"♻️  Reusing cached enhanced preprocessor: {cache_key}")
                
                # 调用增强版处理逻辑
                return enhanced_preprocessor.predict(
                    input,
                    use_doc_orientation_classify,
                    use_doc_unwarping,
                )
            
            # 替换方法
            pipeline._DocPreprocessorPipeline.predict = enhanced_predict
            
            print("✅ DocPreprocessor adapter applied successfully (MinerU algorithm)")
            return True
            
        except Exception as e:
            print(f"❌ Failed to apply DocPreprocessor adapter: {e}")
            import traceback
            traceback.print_exc()
            return False
    
    @classmethod
    def restore(cls):
        """恢复原始方法"""
        if cls._original_predict is None:
            return False
        
        try:
            from paddlex.inference.pipelines.doc_preprocessor import pipeline
            
            pipeline._DocPreprocessorPipeline.predict = cls._original_predict
            cls._original_predict = None
            
            # 🎯 清理共享资源
            cls._shared_ocr_det_model = None
            cls._enhanced_preprocessor_cache.clear()
            
            print("✅ DocPreprocessor adapter restored")
            return True
            
        except Exception as e:
            print(f"❌ Failed to restore DocPreprocessor adapter: {e}")
            return False


# 🎯 便捷函数
def apply_enhanced_doc_preprocessor():
    """应用增强版文档预处理器"""
    return DocPreprocessorAdapter.apply(use_enhanced=True)


def restore_paddlex_doc_preprocessor():
    """恢复 PaddleX 原始文档预处理器"""
    return DocPreprocessorAdapter.restore()


# 导出
__all__ = [
    'EnhancedDocPreprocessor',
    'DocPreprocessorAdapter',
    'apply_enhanced_doc_preprocessor',
    'restore_paddlex_doc_preprocessor',
]