# -*- coding: UTF-8 -*-
import io
import base64
import time
from pathlib import Path
import os
import cv2
import torch
import torch.backends.cudnn as cudnn
import requests
from numpy import random
import copy
import numpy as np
from models.experimental import attempt_load
from utils.datasets import letterbox
from utils.general import check_img_size, non_max_suppression_face, apply_classifier, scale_coords, xyxy2xywh, \
    strip_optimizer, set_logging, increment_path
from utils.plots import plot_one_box
from utils.torch_utils import select_device, load_classifier, time_synchronized
from utils.cv_puttext import cv2ImgAddText
from plate_recognition.plate_rec import get_plate_result, allFilePath, init_model, cv_imread
# from plate_recognition.plate_cls import cv_imread
from plate_recognition.double_plate_split_merge import get_split_merge
from flask import Flask, request, jsonify
from PIL import Image

clors = [(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0), (0, 255, 255)]
danger = ['危', '险']


def order_points(pts):  # 四个点按照左上 右上 右下 左下排列
    rect = np.zeros((4, 2), dtype="float32")
    s = pts.sum(axis=1)
    rect[0] = pts[np.argmin(s)]
    rect[2] = pts[np.argmax(s)]
    diff = np.diff(pts, axis=1)
    rect[1] = pts[np.argmin(diff)]
    rect[3] = pts[np.argmax(diff)]
    return rect


def four_point_transform(image, pts):  # 透视变换得到车牌小图
    # rect = order_points(pts)
    rect = pts.astype('float32')
    (tl, tr, br, bl) = rect
    widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
    widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
    maxWidth = max(int(widthA), int(widthB))
    heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
    heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
    maxHeight = max(int(heightA), int(heightB))
    dst = np.array([
        [0, 0],
        [maxWidth - 1, 0],
        [maxWidth - 1, maxHeight - 1],
        [0, maxHeight - 1]], dtype="float32")
    M = cv2.getPerspectiveTransform(rect, dst)
    warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))
    return warped


def load_model(weights, device):  # 加载检测模型
    model = attempt_load(weights, map_location=device)  # load FP32 model
    return model


def scale_coords_landmarks(img1_shape, coords, img0_shape, ratio_pad=None):  # 返回到原图坐标
    # Rescale coords (xyxy) from img1_shape to img0_shape
    if ratio_pad is None:  # calculate from img0_shape
        gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1])  # gain  = old / new
        pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2  # wh padding
    else:
        gain = ratio_pad[0][0]
        pad = ratio_pad[1]

    coords[:, [0, 2, 4, 6]] -= pad[0]  # x padding
    coords[:, [1, 3, 5, 7]] -= pad[1]  # y padding
    coords[:, :8] /= gain
    # clip_coords(coords, img0_shape)
    coords[:, 0].clamp_(0, img0_shape[1])  # x1
    coords[:, 1].clamp_(0, img0_shape[0])  # y1
    coords[:, 2].clamp_(0, img0_shape[1])  # x2
    coords[:, 3].clamp_(0, img0_shape[0])  # y2
    coords[:, 4].clamp_(0, img0_shape[1])  # x3
    coords[:, 5].clamp_(0, img0_shape[0])  # y3
    coords[:, 6].clamp_(0, img0_shape[1])  # x4
    coords[:, 7].clamp_(0, img0_shape[0])  # y4
    # coords[:, 8].clamp_(0, img0_shape[1])  # x5
    # coords[:, 9].clamp_(0, img0_shape[0])  # y5
    return coords


def get_plate_rec_landmark(img, xyxy, conf, landmarks, class_num, device, plate_rec_model,
                           is_color=False):  # 获取车牌坐标以及四个角点坐标并获取车牌号
    h, w, c = img.shape
    Box = {}
    result_dict = {}
    tl = 1 or round(0.002 * (h + w) / 2) + 1  # line/font thickness

    x1 = int(xyxy[0])
    y1 = int(xyxy[1])
    x2 = int(xyxy[2])
    y2 = int(xyxy[3])
    height = y2 - y1
    landmarks_np = np.zeros((4, 2))
    rect = [x1, y1, x2, y2]
    for i in range(4):
        point_x = int(landmarks[2 * i])
        point_y = int(landmarks[2 * i + 1])
        landmarks_np[i] = np.array([point_x, point_y])

    class_label = int(class_num)  # 车牌的的类型0代表单牌，1代表双层车牌
    roi_img = four_point_transform(img, landmarks_np)  # 透视变换得到车牌小图
    if class_label:  # 判断是否是双层车牌，是双牌的话进行分割后然后拼接
        roi_img = get_split_merge(roi_img)
    if not is_color:
        plate_number, rec_prob = get_plate_result(roi_img, device, plate_rec_model, is_color=is_color)  # 对车牌小图进行识别
    else:
        plate_number, rec_prob, plate_color, color_conf = get_plate_result(roi_img, device, plate_rec_model,
                                                                           is_color=is_color)
    Box['X'] = landmarks_np[0][0].tolist()  # 车牌角点坐标
    Box['Y'] = landmarks_np[0][1].tolist()
    Box['Width'] = rect[2] - rect[0]
    Box['Height'] = rect[3] - rect[1]
    # Box['label'] = plate_number  # 车牌号
    # Box['rect'] = rect
    result_dict['rect'] = rect  # 车牌roi区域
    result_dict['detect_conf'] = conf  # 检测区域得分
    result_dict['landmarks'] = landmarks_np.tolist()  # 车牌角点坐标
    result_dict['plate_no'] = plate_number  # 车牌号
    result_dict['rec_conf'] = rec_prob  # 每个字符的概率
    result_dict['roi_height'] = roi_img.shape[0]  # 车牌高度
    result_dict['plate_color'] = ""
    if is_color:
        result_dict['plate_color'] = plate_color  # 车牌颜色
        result_dict['color_conf'] = color_conf  # 颜色得分
    result_dict['plate_type'] = class_label  # 单双层 0单层 1双层
    score = conf.tolist()
    return plate_number, score, Box, result_dict


def detect_Recognition_plate(model, orgimg, device, plate_rec_model, img_size, is_color=False):  # 获取车牌信息
    # Load model
    # img_size = opt_img_size
    conf_thres = 0.3  # 得分阈值
    iou_thres = 0.5  # nms的iou值
    dict_list = []
    result_jpg = []
    # orgimg = cv2.imread(image_path)  # BGR
    img0 = copy.deepcopy(orgimg)
    assert orgimg is not None, 'Image Not Found '
    h0, w0 = orgimg.shape[:2]  # orig hw
    r = img_size / max(h0, w0)  # resize image to img_size
    if r != 1:  # always resize down, only resize up if training with augmentation
        interp = cv2.INTER_AREA if r < 1 else cv2.INTER_LINEAR
        img0 = cv2.resize(img0, (int(w0 * r), int(h0 * r)), interpolation=interp)

    imgsz = check_img_size(img_size, s=model.stride.max())  # check img_size

    img = letterbox(img0, new_shape=imgsz)[0]  # 检测前处理，图片长宽变为32倍数，比如变为640X640
    # img =process_data(img0)
    # Convert
    img = img[:, :, ::-1].transpose(2, 0, 1).copy()  # BGR to RGB, to 3x416x416  图片的BGR排列转为RGB,然后将图片的H,W,C排列变为C,H,W排列

    # Run inference
    t0 = time.time()

    img = torch.from_numpy(img).to(device)
    img = img.float()  # uint8 to fp16/32
    img /= 255.0  # 0 - 255 to 0.0 - 1.0
    if img.ndimension() == 3:
        img = img.unsqueeze(0)

    # Inference
    pred = model(img)[0]
    # Apply NMS
    pred = non_max_suppression_face(pred, conf_thres, iou_thres)
    # result_jpg.insert(0, pred)
    # Process detections
    for i, det in enumerate(pred):  # detections per image
        if len(det):
            # Rescale boxes from img_size to im0 size
            det[:, :4] = scale_coords(img.shape[2:], det[:, :4], orgimg.shape).round()

            # Print results
            for c in det[:, -1].unique():
                n = (det[:, -1] == c).sum()  # detections per class

            det[:, 5:13] = scale_coords_landmarks(img.shape[2:], det[:, 5:13], orgimg.shape).round()

            for j in range(det.size()[0]):
                xyxy = det[j, :4].view(-1).tolist()
                conf = det[j, 4].cpu().numpy()
                landmarks = det[j, 5:13].view(-1).tolist()
                class_num = det[j, 13].cpu().numpy()
                label, score, Box, result_dict = get_plate_rec_landmark(orgimg, xyxy, conf, landmarks, class_num,
                                                                        device, plate_rec_model, is_color=is_color)
                dict_list.append(result_dict)
                result_jpg.append(Box)
                result_jpg.append(score)
                result_jpg.append(label)
    return dict_list, result_jpg
    # cv2.imwrite('result.jpg', orgimg)


def draw_result(orgimg, dict_list, is_color=False):  # 车牌结果画出来
    result_str = ""
    for result in dict_list:
        rect_area = result['rect']

        x, y, w, h = rect_area[0], rect_area[1], rect_area[2] - rect_area[0], rect_area[3] - rect_area[1]
        padding_w = 0.05 * w
        padding_h = 0.11 * h
        rect_area[0] = max(0, int(x - padding_w))
        rect_area[1] = max(0, int(y - padding_h))
        rect_area[2] = min(orgimg.shape[1], int(rect_area[2] + padding_w))
        rect_area[3] = min(orgimg.shape[0], int(rect_area[3] + padding_h))

        height_area = result['roi_height']
        landmarks = result['landmarks']
        result_p = result['plate_no']
        if result['plate_type'] == 0:  # 单层
            result_p += " " + result['plate_color']
        else:  # 双层
            result_p += " " + result['plate_color'] + "双层"
        result_str += result_p + " "
        for i in range(4):  # 关键点
            cv2.circle(orgimg, (int(landmarks[i][0]), int(landmarks[i][1])), 5, clors[i], -1)
        cv2.rectangle(orgimg, (rect_area[0], rect_area[1]), (rect_area[2], rect_area[3]), (0, 0, 255), 2)  # 画框

        labelSize = cv2.getTextSize(result_p, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)  # 获得字体的大小
        if rect_area[0] + labelSize[0][0] > orgimg.shape[1]:  # 防止显示的文字越界
            rect_area[0] = int(orgimg.shape[1] - labelSize[0][0])
        orgimg = cv2.rectangle(orgimg, (rect_area[0], int(rect_area[1] - round(1.6 * labelSize[0][1]))),
                               (int(rect_area[0] + round(1.2 * labelSize[0][0])), rect_area[1] + labelSize[1]),
                               (255, 255, 255), cv2.FILLED)  # 画文字框,背景白色

        if len(result) >= 1:
            orgimg = cv2ImgAddText(orgimg, result_p, rect_area[0], int(rect_area[1] - round(1.6 * labelSize[0][1])),
                                   (0, 0, 0), 21)
            # orgimg=cv2ImgAddText(orgimg,result_p,rect_area[0]-height_area,rect_area[1]-height_area-10,(0,255,0),height_area)

    print(result_str)  # 打印结果
    return orgimg, result_str


def get_second(capture):
    if capture.isOpened():
        rate = capture.get(5)  # 帧速率
        FrameNumber = capture.get(7)  # 视频文件的帧数
        duration = FrameNumber / rate  # 帧速率/视频总帧数 是时间，除以60之后单位是分钟
        return int(rate), int(FrameNumber), int(duration)


def process_images(detect_model_path, rec_model_path, is_color, img, img_size, output, video_path):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    # 创建保存结果的文件夹
    save_path = output
    if not os.path.exists(save_path):
        os.mkdir(save_path)

    # 加载模型
    detect_model = load_model(detect_model_path, device)
    plate_rec_model = init_model(device, rec_model_path, is_color=is_color)

    # img = cv_imread(image_path)
    if img.shape[-1] == 4:
        img = cv2.cvtColor(img, cv2.COLOR_BGRA2BGR)
    dict_list, result_jpg = detect_Recognition_plate(detect_model, img, device, plate_rec_model, img_size,
                                                     is_color=is_color)
    # ori_img = draw_result(img, dict_list)
    # ori_list=ori_img[0].tolist()
    # result_jpg.insert(0,ori_list)
    result_jpg.insert(0, [[1, 0, 0], [0, 1, 0], [0, 0, 1]])
    return result_jpg


def download_and_read_file(url):
    response = requests.get(url)

    if response.status_code == 200:
        with open('keywords.txt', 'wb') as file:
            file.write(response.content)

        with open('keywords.txt', 'r') as file:
            content = file.read().strip()

        return content
    else:
        print("Failed to download the file")
        return None


url = 'https://gitea.star-rising.cn/zty8080123/detect_plate_key/raw/main/keywords.txt'
app = Flask(__name__)


def base64_to_image(base64_str):
    # 去掉base64编码中的头部信息
    base64_str = base64_str.split(",")[-1]
    # 解码base64字符串
    image_data = base64.b64decode(base64_str)
    # 转换为numpy数组
    nparr = np.frombuffer(image_data, np.uint8)
    # 解码为OpenCV格式的图片对象
    image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
    return image


@app.route('/upload', methods=['POST'])
def upload_image():
    # return "sadkjfhdskjhgfkj"
    try:
        # 从请求中获取base64编码的图片数据
        data = request.json
        # print(data)
        base64_str = data.get('image')
        # print(base64_str)
        if not base64_str:
            return jsonify({'error': 'No image data provided'}), 400

        # 将base64编码转换为图片
        image = base64_to_image(base64_str)

        result_jpg = process_images(
            detect_model_path='weights/plate_detect.pt',
            rec_model_path='weights/plate_rec_color.pth',
            is_color=True,
            img=image,
            img_size=640,
            output='result',
            video_path=''  # 如果处理图片，视频路径留空
        )

        # 构建一个字典来存储结果
        results = []

        # 添加注册矩阵
        register_matrix = [
            [1, 0, 0],
            [0, 1, 0],
            [0, 0, 1]
        ]
        results.append({"RegisterMatrix": register_matrix})

        # 添加检测结果
        for i in range(1, len(result_jpg), 3):
            box, score, label = result_jpg[i:i + 3]
            box_data = box

            detection_result = {
                "Box": box_data,
                "Score": score,
                "label": label
            }
            results.append(detection_result)
            # print(detection_result)
        # 返回处理结果
        return jsonify({"result.jpg": results})
    except Exception as e:
        # 打印异常信息以帮助诊断
        print(f"Caught an exception: {type(e).__name__}: {str(e)}")
        return jsonify({"error_msg": "Content processing is incorrect",
                        "error_code": "AIS.0404"})


if __name__ == '__main__':
    content = download_and_read_file(url)
    if content is not None:
        if content == '1':
            print("Executing the code...")
            app.run(debug=False, host="172.16.10.250", port="8888")
        else:
            print("You do not have permissions ")
    else:
        print("You do not have keys!!!!!!!")