import datetime
import json
import os.path
import time
from io import BytesIO
from PIL import Image
from torchvision import transforms

from CropLand_CD_module.change_detection_module import ChangeDetectionModule
from CropLand_CD_module.visualize_pil_segmentation_mask import visualize_pil_segmentation_mask_opencv
from middleware.conver_segementation_mask import apply_mask_to_image
from middleware.minio_util import downFile, upload_file, upload_file_from_buffer
from mqtt_pub import MQTTClient
# from change_detection_module import ChangeDetectionModule



mqtt_client=None
# MQTT 代理地址和端口
# broker = "112.44.103.230"  # 公共 MQTT 代理（免费）
broker = "8.137.54.85"  # 公共 MQTT 代理（免费）
port = 1883  # MQTT 默认端口
# 主题
topic = "thing/product/ai/events"


def corpland_detection_func(task_id, s3_id, early_pic_url, later_pic_url, model_func_id):
    print("\n=== 变化检测系统 ===")
    gpu_id = "0"
    img_size = 256  # 模型预期的输入尺寸
    lr = 0.0001


    early_pic = downFile(early_pic_url)
    later_pic = downFile(later_pic_url)

    # from PIL import Image

    # image1 = Image.open(image1_path).convert('RGB')
    # image2 = Image.open(image2_path).convert('RGB')
    # image1 = Image.open(early_pic).convert('RGB')
    # image2 = Image.open(later_pic).convert('RGB')
    early_pic_url=os.path.abspath(early_pic)
    later_pic_url=os.path.abspath(later_pic)

    # windows路径
#    model_path = r'D:\project\2025-7-10_data+model\CropLand-CD-main_3\CropLand-CD-main\save_epoch\best_model.pth'
#    save_dir= r"D:\project\2025-7-10_data+model\CropLand-CD-main_3\CropLand-CD-main\save"
    # # linux路径
    model_path = r"CropLand_CD_module/save_epoch/best_model.pth"
    save_dir= r"CropLand_CD_module/save"

    cd = ChangeDetectionModule(gpu_id, img_size, lr)
    cd.load_model(model_path)
    out_save_path = cd.predict_from_imgurl(early_pic_url,later_pic_url,save_dir)
    print(out_save_path)

    pic_path = os.path.abspath(later_pic)
    # src_crs, instance_results_json_path, vis_png, vis_json=visualize_pil_segmentation_mask_opencv(out_save_path,pic_path)
    src_crs, instance_results_json_path, vis_png, _ = visualize_pil_segmentation_mask_opencv(out_save_path, pic_path)

    mask_dir=os.path.dirname(out_save_path)
    mask_name=os.path.basename(out_save_path)
    convert_mask_with_pic_name="mask_"+mask_name
    convert_mask_path=os.path.join(mask_dir,convert_mask_with_pic_name)
    apply_mask_to_image(
        image_path=later_pic_url,
        mask_path=out_save_path,
        output_path=convert_mask_path,
        alpha=0.4
    )

    date_str = datetime.datetime.now().strftime("%Y%m%d")
    time_s = datetime.datetime.now().timestamp()
    mqtt_client = MQTTClient(broker, port, topic)

    pic_name = os.path.basename(later_pic)

    pic_name_1 = f"{date_str}/{time_s}-1-{pic_name}"
    pic_name_2 = f"{date_str}/{time_s}-2-{pic_name}"
    pic_name = f"{date_str}/{time_s}-result-{pic_name}"

    minio_path_1, file_type_1 = upload_file(early_pic, None)
    minio_path_2, file_type_2 = upload_file(later_pic, None)
    minio_path_result, file_type_result = upload_file(convert_mask_path, None)
    minio_vis_png, file_type_result = upload_file(vis_png, None)
    # if instance_results_json_path and os.path.exists(instance_results_json_path):
    #     # minio_vis_json, file_type_result = upload_file(json_result_path, "application/json")
    #     minio_vis_json, file_type_result = upload_file(instance_results_json_path, None)
    # # minio_path_result, file_type_result = upload_file_from_buffer(buffer, pic_name)
    if instance_results_json_path is None or not os.path.exists(instance_results_json_path):
        print("警告: 无法生成有效的JSON结果文件，使用空文件替代")
        # 创建一个空的JSON文件作为替代
        empty_result = {"error": "无法生成有效的结果", "instances": []}
        instance_results_json_path = os.path.join(os.path.dirname(out_save_path), "empty_result.json")
        with open(instance_results_json_path, 'w') as f:
            json.dump(empty_result, f)
    minio_vis_json, file_type_result = upload_file(instance_results_json_path, None)


    message = {
        "task_id": task_id,
        "minio": {
            "minio_path_1": minio_path_1,
            "minio_path_2": minio_path_2,
            "minio_path_result": minio_path_result,
            "minio_path_boundary": minio_vis_png,
            "minio_path_json": minio_vis_json,
            "crs_info":str(src_crs),
            "file_type": file_type_result
        }
    }
    json_message = json.dumps(message, indent=4, ensure_ascii=False)
    mqtt_client.publish_message(json_message)
    time.sleep(0.5)
    # 删除一期
    if os.path.exists(early_pic):
        os.remove(early_pic)
        # 删除二期
    if os.path.exists(later_pic):
        os.remove(later_pic)
        # 删除掩码
    if os.path.exists(out_save_path):
        os.remove(out_save_path)
        # 删除掩码覆盖原图
    if os.path.exists(convert_mask_path):
        os.remove(convert_mask_path)
    #     删除本地掩码图片
    if os.path.exists(minio_vis_png):
        os.remove(minio_vis_png)
    #     删除本地json文件
    if os.path.exists(minio_vis_json):
        os.remove(minio_vis_json)
    # 删除修改经纬度之后的json文件
    if os.path.exists(instance_results_json_path):
        os.remove(instance_results_json_path)

# if __name__ == '__main__':