from lib.qwen_fun import hazard_inspection, search_knowledge_base
from encodings.punycode import T
"""
测试 在给定标注框与类别，原始视频经过转换之后，AI能否准确识别物体特征
"""

from tkinter import N
from datetime import datetime
import json
import os
from lib.qwen_fun import chat, get_annnotated_frame_for_ai, get_unique_track_id_count, save_json_to_file, search_knowledge_base, upload_files_and_get_urls_concurrently
from lib.qwen_fun_vid import create_mian_vid_for_ai, frame_all_to_obj_vid
from lib.sam3 import SAM3
from ultralytics.models.sam import SAM3VideoSemanticPredictor
import cv2
import json
import numpy as np
from pathlib import Path

def run(output_dir, vid_path, interval: int = 5):
    if output_dir:
        os.makedirs(output_dir, exist_ok=True)  # exist_ok=True 防止重复创建报错

    annotated_frames: SAM3 = SAM3()
    annotated_frames.run(vid_path, output_dir, "lib/class_list/xiaofangz.json")
    # annotated_frames.load_from_json(f"{output_dir}/frame_all.json")
    # print(annotated_frames.data)

    # 提取ai能看到的部分
    ai_frames: dict = get_annnotated_frame_for_ai(annotated_frames.data(), interval)
    save_json_to_file(ai_frames, f"{output_dir}/frame_all_ai.json")


    if vid_path.startswith("oss"):
        video_url = vid_path
    else:
        video_url: str|None = upload_files_and_get_urls_concurrently(
            file_path_list=[vid_path],
            max_workers=8
        )[0]
        if video_url:
            with open(f"{output_dir}/video_url.json", "w") as f:
                f.write(video_url)

    if video_url is None:
        raise ValueError("视频上传失败，无法获取 URL")

    result = hazard_inspection(ai_frames, video_url)[1]

    with open(f"{output_dir}/hazard_inspection.json", "w", encoding="utf-8") as f:
        f.write(result)

# 启动应用
if __name__ == "__main__":
    VID_NAME: str = "Peihdianhxiang"
    VID_END: str = ".mp4"
    interval: int = int(30 / 5)

    output_dir: str = f"output/{VID_NAME}"
    vid_path: str = f"./input/{VID_NAME}{VID_END}"

    run(output_dir, vid_path, interval)