import time

from models.line_detect.postprocess import show_predict
import os

import torch
from PIL import Image
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
from models.line_detect.line_net import linenet_resnet50_fpn
from torchvision import transforms
from rtree import index
# from models.wirenet.postprocess import postprocess
from models.wirenet.postprocess import postprocess

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')


def load_best_model(model, save_path, device):
    if os.path.exists(save_path):
        checkpoint = torch.load(save_path, map_location=device)
        model.load_state_dict(checkpoint['model_state_dict'])
        # if optimizer is not None:
        #     optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
        epoch = checkpoint['epoch']
        loss = checkpoint['loss']
        print(f"Loaded best model from {save_path} at epoch {epoch} with loss {loss:.4f}")
    else:
        print(f"No saved model found at {save_path}")
    return model


def box_line_optimized(pred):
    # 创建R-tree索引
    idx = index.Index()

    # 将所有线段添加到R-tree中
    lines = pred[-1]['wires']['lines']  # 形状为[1, 2500, 2, 2]
    scores = pred[-1]['wires']['score'][0]  # 假设形状为[2500]

    # 提取并处理所有线段
    for idx_line in range(lines.shape[1]):  # 遍历2500条线段
        line_tensor = lines[0, idx_line].cpu().numpy() / 128 * 512  # 转换为numpy数组并调整比例
        x_min = float(min(line_tensor[0][0], line_tensor[1][0]))
        y_min = float(min(line_tensor[0][1], line_tensor[1][1]))
        x_max = float(max(line_tensor[0][0], line_tensor[1][0]))
        y_max = float(max(line_tensor[0][1], line_tensor[1][1]))
        idx.insert(idx_line, (x_min, y_min, x_max, y_max))

    for idx_box, box_ in enumerate(pred[0:-1]):
        box = box_['boxes'].cpu().numpy()  # 确保将张量转换为numpy数组
        line_ = []
        score_ = []

        for i in box:
            score_max = 0.0
            tmp = [[0.0, 0.0], [0.0, 0.0]]

            # 获取与当前box可能相交的所有线段
            possible_matches = list(idx.intersection((i[0], i[1], i[2], i[3])))

            for j in possible_matches:
                line_j = lines[0, j].cpu().numpy() / 128 * 512
                if (line_j[0][1] >= i[0] and line_j[1][1] >= i[0] and  # 注意这里交换了x和y
                        line_j[0][1] <= i[2] and line_j[1][1] <= i[2] and
                        line_j[0][0] >= i[1] and line_j[1][0] >= i[1] and
                        line_j[0][0] <= i[3] and line_j[1][0] <= i[3]):

                    if scores[j] > score_max:
                        tmp = line_j
                        score_max = scores[j]

            line_.append(tmp)
            score_.append(score_max)

        processed_list = torch.tensor(line_)
        pred[idx_box]['line'] = processed_list

        processed_s_list = torch.tensor(score_)
        pred[idx_box]['line_score'] = processed_s_list

    return pred

# def box_line_(pred):
#     for idx, box_ in enumerate(pred[0:-1]):
#         box = box_['boxes']  # 是一个tensor
#         line = pred[-1]['wires']['lines'][idx].cpu().numpy() / 128 * 512
#         score = pred[-1]['wires']['score'][idx]
#         line_ = []
#         score_ = []
#
#         for i in box:
#             score_max = 0.0
#             tmp = [[0.0, 0.0], [0.0, 0.0]]
#
#             for j in range(len(line)):
#                 if (line[j][0][1] >= i[0] and line[j][1][1] >= i[0] and
#                         line[j][0][1] <= i[2] and line[j][1][1] <= i[2] and
#                         line[j][0][0] >= i[1] and line[j][1][0] >= i[1] and
#                         line[j][0][0] <= i[3] and line[j][1][0] <= i[3]):
#
#                     if score[j] > score_max:
#                         tmp = line[j]
#                         score_max = score[j]
#             line_.append(tmp)
#             score_.append(score_max)
#         processed_list = torch.tensor(line_)
#         pred[idx]['line'] = processed_list
#
#         processed_s_list = torch.tensor(score_)
#         pred[idx]['line_score'] = processed_s_list
#     return pred


def predict(pt_path, model, img):
    model = load_best_model(model, pt_path, device)

    model.eval()

    if isinstance(img, str):
        img = Image.open(img).convert("RGB")

    transform = transforms.ToTensor()
    img_tensor = transform(img)

    with torch.no_grad():
        t_start = time.time()
        predictions = model([img_tensor.to(device)])
        t_end=time.time()
        print(f'predict used:{t_end-t_start}')
        # print(f'predictions:{predictions}')
        boxes=predictions[0]['boxes'].shape
        lines=predictions[-1]['wires']['lines'].shape
        lines_scores=predictions[-1]['wires']['score'].shape
        print(f'predictions boxes:{boxes},lines:{lines},lines_scores:{lines_scores}')
    t_start=time.time()
    pred = box_line_optimized(predictions)
    t_end=time.time()
    print(f'matched boxes and lines used:{t_end - t_start}')
    # print(f'pred:{pred[0]}')
    show_predict(img_tensor, pred, t_start)


if __name__ == '__main__':
    t_start = time.time()
    print(f'start to predict:{t_start}')
    model = linenet_resnet50_fpn().to(device)
    pt_path = r"F:\BaiduNetdiskDownload\resnet50_best_e8.pth"
    img_path = r"I:\datasets\wirenet_1000\images\val\00035148_0.png"
    predict(pt_path, model, img_path)
    t_end = time.time()
    # print(f'predict used:{t_end - t_start}')