from analysis_result.get_model_result import det_img
from analysis_result.same_model_img import same_model_img_analysis_labels, model_labels_selet
from model_load.model_load import load_model
from drawing_img.drawing_img import drawing_frame
from analysis_data.data_dir_file import get_dir_file
from analysis_data.config_load import get_configs
from utils import is_image_file, is_rtsp_or_video

import cv2
import os
import time
from datetime import datetime


def data_load(args):

    # print('正在运行的进程',msg)
    # print(args)
    source = args[0]
    model_yaml = args[1]

    # 数据加载
    rtsp_or_video_source = is_rtsp_or_video(source)
    dir_source = os.path.isdir(source)
    img_source = is_image_file(source)

    # # 模型加载
    model_data = get_configs(model_yaml)
    model_inference = load_model(model_file=model_data["model"],
                                 device=model_data["model_parameter"]['device'],
                                 cache_file=model_data["model_cache"])

    if rtsp_or_video_source:

        cap = cv2.VideoCapture(source)

        try:
            i = 0
            while True:
                ret, frame = cap.read()

                if not ret:
                    continue  # 如果未成功读取到视频帧，则继续读取下一帧
                print(source,datetime.today(), i)
                # if source == 'rtsp://admin:@192.168.10.18':

                #     cv2.imshow('18',frame)

                img_frame_dict = {"path": source, 'frame': frame}
                images_update = img_process(
                    img_frame_dict, model_inference, model_data)
                
                # print(type(images_update['frame']))

                # if source == 'rtsp://admin:@192.168.10.18':
                #     cv2.namedWindow('18',0)
                #     cv2.imshow('18',images_update['frame'])

                i = i+1
                

        except Exception as e:
            # 处理异常或错误
            print(str(e))

        cap.release()

    if dir_source:

        img_ext = [".jpg", ".JPG", ".bmp"]
        video_ext = [".mp4", ".avi", ".MP4"]

        img_list = get_dir_file(source, img_ext)
        video_list = get_dir_file(source, video_ext)

        if img_list:

            for img in img_list:

                t1 = time.time()
                images = cv2.imread(img)   
 
                img_frame_dict = {"path": img, 'frame': images}

                images_update = img_process(
                    img_frame_dict, model_inference, model_data)

                t2 = time.time()
                tx = t2 - t1
                print('检测一张图片的时间为：',tx)


        if video_list:

            pass

    if img_source:

        img_para = True

        if img_para:
            images = cv2.imread(source)
            img_frame_dict = {"path": source, 'frame': images}

            images_update = img_process(
                img_frame_dict, model_inference, model_data)


def img_process(images, model_inference, model_data):

    start_point = time.perf_counter()

    # 检测每帧图片，返回推理结果
    results = det_img(model_inference=model_inference,
                      images_frame=images['frame'],
                      confidence=model_data["model_parameter"]['confidence'],
                      label_name_list=model_data["model_parameter"]['label_names'])

    # print(images['path'])

    # 根据需要挑选标注框信息
    select_labels_list = model_labels_selet(example_list=model_data["model_parameter"]['compara_label_names'],
                                            result_dict_list=results)

    if model_data["model_parameter"]['compara_relevancy']:

        # 需要根据的逻辑判断标注框信息
        determine_bbox = same_model_img_analysis_labels(example_list=model_data["model_parameter"]['compara_label_names'],
                                                        result_dicts_list=select_labels_list,
                                                        relevancy=model_data["model_parameter"]['compara_relevancy'],
                                                        relevancy_para=model_data["model_parameter"]['relevancy_para'])
    else:

        determine_bbox = select_labels_list

    # 判断后的信息返回结果，这里是画图，可返回结果
    if determine_bbox:

        images.update({"results": determine_bbox})

        img_save = drawing_frame(
            images_frame=images['frame'], result_list=determine_bbox)
        
        images.update({"frame": img_save})

        img_name = images_save(
            images=images['frame'], save_path=model_data["save_path"])

        print('sleep:', images['path'], img_name)

        if model_data['save_path_original']:
            images_save(images=images['frame'],
                        save_path=model_data["save_path_original"])
        else:
            pass
    else:
        # 没检测出来的图片是否保存
        if model_data["test_path"]:
            img_name = images_save(
                images=images['frame'], save_path=model_data["test_path"])
            # print('no:',images['path'],img_name)

        else:
            pass
    
    # 展示显示
    # if images['path'] == 'rtsp://admin:@192.168.10.11':
    #     cv2.namedWindow('11', cv2.WINDOW_NORMAL)
    #     cv2.imshow('11',images['frame'])
    #     cv2.waitKey(1)
        # cv2.destroyAllWindows()

    # t2 = time.time()

    end_point = time.perf_counter()
    cost = end_point - start_point

    print(f"Predicted in {cost * 1000:.2f}ms. {1.0 / cost:.2f} FPS")

    return images


def images_save(images, save_path):
    # 保存时候时间为图片名
    data_now = datetime.today()
    images_name = str(data_now.year) + str(data_now.month) + str(data_now.day) + str(data_now.hour) + \
        str(data_now.minute) + str(data_now.second) + \
        str(data_now.microsecond) + '.jpg'
    img_save_path = save_path + '/' + str(
        data_now.year) + '/' + str(data_now.month) + '_' + str(data_now.day) + '/'

    if not os.path.exists(img_save_path):
        os.makedirs(img_save_path)

    full_name = img_save_path + images_name

    cv2.imwrite(full_name, images)

    return full_name