import os
import torch
import cv2
import argparse
import numpy as np
from pprint import pprint
from tqdm import tqdm

from middleware.minio_util import downFile
from mmseg.apis import init_model, inference_model

"""

"""

DEVICE = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
# 测试图像所在文件夹
# IMAGE_FILE_PATH = r"D:\project\UAV_model/need_prediction"
IMAGE_FILE_PATH = r"C:\Users\14867\Desktop\seg_tiff"
# 模型训练结果的config配置文件路径
CONFIG = r'unetformer_UAV_6000X4000.py'
# 模型训练结果的权重文件路径
CHECKPOINT = r'best_mIoU_iter_20000.pth'
# 模型推理测试结果的保存路径，每个模型的推理结果都保存在`{save_dir}/{模型config同名文件夹}`下，如文末图片所示。
SAVE_DIR = r"uv_predict_result"


def make_full_path(root_list, root_path):
    file_full_path_list = []
    for filename in root_list:
        file_full_path = os.path.join(root_path, filename)
        file_full_path_list.append(file_full_path)
    return file_full_path_list


def read_filepath(root):
    from natsort import natsorted
    test_image_list = natsorted(os.listdir(root))
    test_image_full_path_list = make_full_path(test_image_list, root)
    return test_image_full_path_list


from PIL import Image


def save_colored_prediction(predictions, save_path):
    # color_map = [
    # [0, 0, 0],
    # [165, 42, 42],
    # [0, 255, 0],2
    # [255, 255, 0],
    # [0, 0, 255],
    # [128, 128, 128],
    # [0, 255, 255]
    # ]

    color_map = [
        [255, 0, 0],  # 类别0: 红色
        [0, 255, 0],  # 类别1: 绿色
        [0, 0, 255],  # 类别2: 蓝色
        [255, 255, 0],  # 类别3: 黄色
        [255, 0, 255],  # 类别4: 品红
        [0, 255, 255],  # 类别5: 青色
        [128, 0, 128]  # 类别6: 紫色
    ]

    # 类别 0：黑色 ；背景
    # 类别 1：棕色 ；荒地
    # 类别 2：绿色 ；林地
    # 类别 3：黄色 ；农田
    # 类别 4：蓝色 ；水域
    # 类别 5：灰色 ；道路
    # 类别 6：青色 ；建筑用地

    # 创建一个空的 RGB 图像
    colored_image = np.zeros((predictions.shape[0], predictions.shape[1], 3), dtype=np.uint8)

    # 将每个类别的颜色赋值到图像
    for class_id in range(len(color_map)):
        colored_image[predictions == class_id] = color_map[class_id]

    # 转换为 PIL 图像并保存
    image = Image.fromarray(colored_image)
    image.save(save_path)

#
# def predict(taskid, ts, img_url_list):
#     model_mmseg = init_model(CONFIG, CHECKPOINT, device=DEVICE)
#     result_img_list = []
#     for imgs in tqdm(img_url_list):
#         result = inference_model(model_mmseg, imgs)
#         pred_mask = result.pred_sem_seg.data.squeeze(0).detach().cpu().numpy().astype(np.uint8)
#
#         # save_path = os.path.join(SAVE_DIR, f"{os.path.basename(CONFIG).split('.')[0]}")
#         # if not os.path.exists(save_path):
#         #     os.makedirs(save_path)
#         saves_path = os.path.join(SAVE_DIR, f"{os.path.basename(result.img_path).split('.')[0]}.png")
#         save_colored_prediction(pred_mask, saves_path)
#         result_img_list.append(saves_path)
#
#     if len(result_img_list) > 0:
#         return result_img_list
#     else:
#         return None
#
#         #
#         # pred_mask[pred_mask == 1] = 255
#         # save_path = os.path.join(args.save_dir, f"{os.path.basename(args.config).split('.')[0]}")
#         #
#         # if not os.path.exists(save_path):
#         #     os.makedirs(save_path)
#         #
#         # cv2.imwrite(os.path.join(save_path, f"{os.path.basename(result.img_path).split('.')[0]}.png"), pred_mask,
#         #             [cv2.IMWRITE_PNG_COMPRESSION, 0])
#


# 做单张图片推理，不然几个图片不好做组合
def predict_pic(taskid, ts, img_url):
    model_mmseg = init_model(CONFIG, CHECKPOINT, device=DEVICE)
    result_img_list = []

    result = inference_model(model_mmseg, img_url)
    pred_mask = result.pred_sem_seg.data.squeeze(0).detach().cpu().numpy().astype(np.uint8)
    saves_path = os.path.join(SAVE_DIR, f"{os.path.basename(result.img_path).split('.')[0]}.png")
    save_colored_prediction(pred_mask, saves_path)
    # result_img_list.append(saves_path)
    return saves_path



if __name__ == '__main__':
    flight_task_id = "7a5c83e0-fe0d-47bf-a8e1-9bd663508783"  # 任务id
    list_s3_url = [
        "test/patch_0011.png",
        "test/patch_0012.png"
    ]  # s3 img 地址集合
    local_img_url_list=[]
    for img_url in list_s3_url:
        pic = downFile(img_url)
        pic_path=os.path.abspath(pic)
        local_img_url_list.append(pic_path)
    list_func_id =[20000,20001]  # 方法id集合
    predict(flight_task_id,12,local_img_url_list)