import torch
import torch.nn as nn
import numpy as np
import cv2
from torch.autograd import Variable
import os


# 字符集定义
CHARS = ['京', '沪', '津', '渝', '冀', '晋', '蒙', '辽', '吉', '黑',
         '苏', '浙', '皖', '闽', '赣', '鲁', '豫', '鄂', '湘', '粤',
         '桂', '琼', '川', '贵', '云', '藏', '陕', '甘', '青', '宁',
         '新',
         '0', '1', '2', '3', '4', '5', '6', '7', '8', '9',
         'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'J', 'K',
         'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'U', 'V',
         'W', 'X', 'Y', 'Z', 'I', 'O', '-'
         ]

CHARS_DICT = {char: i for i, char in enumerate(CHARS)}

# 全局变量
lprnet_model = None
device = None

class small_basic_block(nn.Module):
    def __init__(self, ch_in, ch_out):
        super(small_basic_block, self).__init__()
        self.block = nn.Sequential(
            nn.Conv2d(ch_in, ch_out // 4, kernel_size=1),
            nn.ReLU(),
            nn.Conv2d(ch_out // 4, ch_out // 4, kernel_size=(3, 1), padding=(1, 0)),
            nn.ReLU(),
            nn.Conv2d(ch_out // 4, ch_out // 4, kernel_size=(1, 3), padding=(0, 1)),
            nn.ReLU(),
            nn.Conv2d(ch_out // 4, ch_out, kernel_size=1),
        )
    
    def forward(self, x):
        return self.block(x)

class LPRNet(nn.Module):
    def __init__(self, lpr_max_len, phase, class_num, dropout_rate):
        super(LPRNet, self).__init__()
        self.phase = phase
        self.lpr_max_len = lpr_max_len
        self.class_num = class_num
        self.backbone = nn.Sequential(
            nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, stride=1), # 0
            nn.BatchNorm2d(num_features=64),
            nn.ReLU(),  # 2
            nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(1, 1, 1)),
            small_basic_block(ch_in=64, ch_out=128),    # *** 4 ***
            nn.BatchNorm2d(num_features=128),
            nn.ReLU(),  # 6
            nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(2, 1, 2)),
            small_basic_block(ch_in=64, ch_out=256),   # 8
            nn.BatchNorm2d(num_features=256),
            nn.ReLU(),  # 10
            small_basic_block(ch_in=256, ch_out=256),   # *** 11 ***
            nn.BatchNorm2d(num_features=256),   # 12
            nn.ReLU(),
            nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(4, 1, 2)),  # 14
            nn.Dropout(dropout_rate),
            nn.Conv2d(in_channels=64, out_channels=256, kernel_size=(1, 4), stride=1),  # 16
            nn.BatchNorm2d(num_features=256),
            nn.ReLU(),  # 18
            nn.Dropout(dropout_rate),
            nn.Conv2d(in_channels=256, out_channels=class_num, kernel_size=(13, 1), stride=1), # 20
            nn.BatchNorm2d(num_features=class_num),
            nn.ReLU(),  # *** 22 ***
        )
        self.container = nn.Sequential(
            nn.Conv2d(in_channels=448+self.class_num, out_channels=self.class_num, kernel_size=(1, 1), stride=(1, 1)),
        )

    def forward(self, x):
        keep_features = list()
        for i, layer in enumerate(self.backbone.children()):
            x = layer(x)
            if i in [2, 6, 13, 22]: # [2, 4, 8, 11, 22]
                keep_features.append(x)

        global_context = list()
        for i, f in enumerate(keep_features):
            if i in [0, 1]:
                f = nn.AvgPool2d(kernel_size=5, stride=5)(f)
            if i in [2]:
                f = nn.AvgPool2d(kernel_size=(4, 10), stride=(4, 2))(f)
            f_pow = torch.pow(f, 2)
            f_mean = torch.mean(f_pow)
            f = torch.div(f, f_mean)
            global_context.append(f)

        x = torch.cat(global_context, 1)
        x = self.container(x)
        logits = torch.mean(x, dim=2)

        return logits

def build_lprnet(lpr_max_len=8, phase=False, class_num=66, dropout_rate=0.5):
    """构建LPRNet模型"""
    Net = LPRNet(lpr_max_len, phase, class_num, dropout_rate)
    
    if phase == "train":
        return Net.train()
    else:
        return Net.eval()

def preprocess_image(image_array, img_size=(94, 24)):
    """图像预处理"""
    # 确保输入是numpy数组
    if not isinstance(image_array, np.ndarray):
        raise ValueError("输入必须是numpy数组")
    
    # 调整图像尺寸
    height, width = image_array.shape[:2]
    if height != img_size[1] or width != img_size[0]:
        image_array = cv2.resize(image_array, img_size)
    
    # 归一化到[0,1]
    image_array = image_array.astype(np.float32) / 255.0
    
    # 转换为CHW格式
    if len(image_array.shape) == 3:
        image_array = np.transpose(image_array, (2, 0, 1))
    
    # 添加batch维度
    image_array = np.expand_dims(image_array, axis=0)
    
    return image_array

def greedy_decode(prebs):
    """贪婪解码"""
    preb_labels = list()
    for i in range(prebs.shape[0]):
        preb = prebs[i, :, :]
        preb_label = list()
        for j in range(preb.shape[1]):
            preb_label.append(np.argmax(preb[:, j], axis=0))
        
        no_repeat_blank_label = list()
        pre_c = preb_label[0]
        if pre_c != len(CHARS) - 1:
            no_repeat_blank_label.append(pre_c)
        
        for c in preb_label:  # 去除重复标签和空白标签
            if (pre_c == c) or (c == len(CHARS) - 1):
                if c == len(CHARS) - 1:
                    pre_c = c
                continue
            no_repeat_blank_label.append(c)
            pre_c = c
        
        preb_labels.append(no_repeat_blank_label)
    
    return preb_labels

def LPRNinitialize_model(model_path=None):
    """初始化LPRNet模型"""
    global lprnet_model, device
    
    try:
        # 设置设备
        device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
        print(f"使用设备: {device}")
        
        # 构建模型
        lprnet_model = build_lprnet(
            lpr_max_len=8, 
            phase=False, 
            class_num=len(CHARS), 
            dropout_rate=0.5
        )
        
        # 加载预训练权重
        if model_path is None:
            model_path = os.path.join(os.path.dirname(__file__), "Final_LPRNet_model.pth")
        
        if os.path.exists(model_path):
            checkpoint = torch.load(model_path, map_location=device)
            lprnet_model.load_state_dict(checkpoint)
            print(f"成功加载预训练模型: {model_path}")
        else:
            print(f"警告: 未找到预训练模型文件 {model_path}，使用随机初始化权重")
        
        lprnet_model.to(device)
        lprnet_model.eval()
        
        print("LPRNet模型初始化完成")
        
        # 统计模型参数
        total_params = sum(p.numel() for p in lprnet_model.parameters())
        print(f"LPRNet模型参数数量: {total_params:,}")
        
        return True
        
    except Exception as e:
        print(f"LPRNet模型初始化失败: {e}")
        import traceback
        traceback.print_exc()
        return False

def LPRNmodel_predict(image_array):
    """
    LPRNet车牌号识别接口函数
    
    参数:
        image_array: numpy数组格式的车牌图像，已经过矫正处理
    
    返回:
        list: 包含7个字符的列表，代表车牌号的每个字符
              例如: ['京', 'A', '1', '2', '3', '4', '5']
    """
    global lprnet_model, device
    
    if lprnet_model is None:
        print("LPRNet模型未初始化，请先调用LPRNinitialize_model()")
        return ['待', '识', '别', '0', '0', '0', '0']
    
    try:
        # 预处理图像
        processed_image = preprocess_image(image_array)
        
        # 转换为tensor
        input_tensor = torch.from_numpy(processed_image).float()
        input_tensor = input_tensor.to(device)
        
        # 模型推理
        with torch.no_grad():
            prebs = lprnet_model(input_tensor)
            prebs = prebs.cpu().detach().numpy()
        
        # 贪婪解码
        preb_labels = greedy_decode(prebs)
        
        if len(preb_labels) > 0 and len(preb_labels[0]) > 0:
            # 将索引转换为字符
            predicted_chars = [CHARS[idx] for idx in preb_labels[0] if idx < len(CHARS)]
            
            print(f"LPRNet识别结果: {''.join(predicted_chars)}")
            
            # 确保返回7个字符（车牌标准长度）
            if len(predicted_chars) < 7:
                # 如果识别结果少于7个字符，用'0'补齐
                predicted_chars.extend(['0'] * (7 - len(predicted_chars)))
            elif len(predicted_chars) > 7:
                # 如果识别结果多于7个字符，截取前7个
                predicted_chars = predicted_chars[:7]
            
            return predicted_chars
        else:
            print("LPRNet识别结果为空")
            return ['识', '别', '为', '空', '0', '0', '0']
            
    except Exception as e:
        print(f"LPRNet识别失败: {e}")
        import traceback
        traceback.print_exc()
        return ['识', '别', '失', '败', '0', '0', '0']

# 为了保持与其他模块的一致性，提供一个处理器类
class LPRProcessor:
    def __init__(self):
        self.initialized = False
    
    def initialize(self, model_path=None):
        """初始化模型"""
        self.initialized = LPRNinitialize_model(model_path)
        return self.initialized
    
    def predict(self, image_array):
        """预测接口"""
        if not self.initialized:
            print("模型未初始化")
            return ['未', '初', '始', '化', '0', '0', '0']
        return LPRNmodel_predict(image_array)

# 创建全局处理器实例
_processor = LPRProcessor()

def get_lpr_processor():
    """获取LPR处理器实例"""
    return _processor