302 lines
11 KiB
Python
302 lines
11 KiB
Python
from lib.qwen_fun import get_annnotated_frame_for_ai_without_xyxy, hazard_inspection, merge_conflict_inspection_data, report_generator, search_knowledge_base
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from encodings.punycode import T
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"""
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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
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"""
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from tkinter import N
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from datetime import datetime
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import json
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import os
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from lib.qwen_fun import save_json_to_file, upload_files_and_get_urls_concurrently
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from lib.sam3 import SAM3
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import cv2
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import json
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import numpy as np
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from pathlib import Path
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import gradio as gr
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fps = 0
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<<<<<<< HEAD
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VIDEO_FOLDER: str = "input"
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def load_file_list() -> list[str]:
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"""
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加载指定文件夹下的所有文件名称（不含子目录）
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返回：文件名列表
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"""
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file_names: list[str] = []
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if not os.path.isdir(VIDEO_FOLDER):
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return file_names
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for item in os.listdir(VIDEO_FOLDER):
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item_path: str = os.path.join(VIDEO_FOLDER, item)
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if os.path.isfile(item_path):
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file_names.append(item)
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return file_names
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def get_full_vid_path(vid_file: str) -> str:
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"""
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获取视频绝对路径
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"""
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full_path: str = os.path.join(os.getcwd(), VIDEO_FOLDER, vid_file)
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gr.Info(full_path)
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return full_path
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def update_preview(frame_idx: int, vid_file: str):
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vid_name: str = Path(vid_file).stem
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=======
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def update_preview(frame_idx: int, vid_name: str):
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>>>>>>> 7562de4 (2 预览图还有点问题)
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output_dir: str = f"output/{vid_name}"
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global fps
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idx = int(frame_idx // fps)
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img_path = f"{output_dir}/boxes/frame_{idx:04d}.jpg"
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with open(f"{output_dir}/hazard_inspection.json", "r", encoding="utf-8") as f:
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result = f.read()
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dic = json.loads(result)
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class_tag = get_class_tag_by_frame(dic, frame_idx, fps)
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return img_path, class_tag
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def run(vid_file: str, run_sam3: bool = True, run_inspection: bool = True, gen_report: bool = True,):
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#================初始化=================
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vid_name: str = Path(vid_file).stem
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vid_end: str = Path(vid_file).suffix
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vid_path: str = f"./input/{vid_name}{vid_end}"
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cap = cv2.VideoCapture(vid_path)
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global fps
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fps = int(cap.get(cv2.CAP_PROP_FPS)) # 获取视频FPS
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cap.release()
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output_dir: str = f"output/{vid_name}"
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# interval = int(fps / 5)
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interval = fps
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conf = 0.7
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time_data: dict = {}
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annotated_frames: SAM3 = SAM3()
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result: dict = {}
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if output_dir:
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os.makedirs(output_dir, exist_ok=True) # exist_ok=True 防止重复创建报错
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#================获取物体信息=================
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# 保存开始时间字符串
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time_data["start_time"] = str(datetime.now())
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with open(f"{output_dir}/time.json", "w") as f:
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f.write(json.dumps(time_data, ensure_ascii=False, indent=4))
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if run_sam3:
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# 针对厂房防火分区
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annotated_frames.run(vid_path, output_dir, "lib/class_list/1.厂房防火.json", interval, conf)
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else:
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annotated_frames.load_from_json(f"{output_dir}/frame_all.json")
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# print(annotated_frames.data)
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# 提取ai能看到的部分
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ai_frames: dict = get_annnotated_frame_for_ai_without_xyxy(annotated_frames.data(), 1, conf)
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save_json_to_file(ai_frames, f"{output_dir}/frame_all_ai.json")
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#================隐患检查=================
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if run_inspection:
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if vid_path.startswith("oss"):
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video_url = vid_path
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else:
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video_url: str|None = None
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video_url_file = f"{output_dir}/video_url.json"
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# 检查URL是否已存在
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if os.path.exists(video_url_file):
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try:
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with open(video_url_file, "r", encoding="utf-8") as f:
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url_data = json.load(f)
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if vid_name in url_data:
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video_url = url_data[vid_name]
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print(f"使用已存在的URL: {video_url}")
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except Exception as e:
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print(f"读取URL文件失败: {e}")
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# 如果URL不存在，上传文件
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if video_url is None:
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print(f"上传视频文件: {vid_path}")
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video_url = upload_files_and_get_urls_concurrently(
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file_path_list=[vid_path],
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max_workers=8
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)[0]
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if video_url:
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# 保存为JSON格式，包含文件名和URL的键值对
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url_data = {}
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if os.path.exists(video_url_file):
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try:
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with open(video_url_file, "r", encoding="utf-8") as f:
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url_data = json.load(f)
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except:
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pass
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url_data[vid_name] = video_url
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with open(video_url_file, "w", encoding="utf-8") as f:
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json.dump(url_data, f, ensure_ascii=False, indent=4)
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print(f"URL已保存: {video_url}")
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if video_url is None:
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raise ValueError("视频上传失败，无法获取 URL")
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<<<<<<< HEAD
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result_test: str
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reason_test: str
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reason_test, result_test = hazard_inspection(ai_frames, video_url, enable_thinking=True, fps=2)
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result = json.loads(result_test)
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=======
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result = json.loads(hazard_inspection(ai_frames, video_url, enable_thinking=False, fps=2)[1])
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>>>>>>> 7562de4 (2 预览图还有点问题)
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# merged_result = merge_conflict_inspection_data(result)
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result["class"] = ai_frames["class_list"]
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with open(f"{output_dir}/hazard_inspection.json", "w", encoding="utf-8") as f:
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f.write(json.dumps(result, ensure_ascii=False, indent=4))
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with open(f"{output_dir}/hazard_inspection_reason.json", "w", encoding="utf-8") as f:
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f.write(json.dumps(reason_test, ensure_ascii=False, indent=4))
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# with open(f"{output_dir}/hazard_inspection_merged.json", "w", encoding="utf-8") as f:
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# f.write(json.dumps(merged_result, ensure_ascii=False, indent=4))
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#================生成报告=================
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if gen_report:
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if result == {}:
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with open(f"{output_dir}/hazard_inspection.json", "r", encoding="utf-8") as f:
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result = json.load(f)
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with open(f"知识库/rule.json", "r", encoding="utf-8") as f:
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rule_definitions = json.load(f)
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report_generator(
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video_path=vid_path, # 视频文件路径
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detection_data=annotated_frames.data(), # 物体检测数据
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hazard_results=result, # 隐患检查结果
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rule_definitions=rule_definitions, # 规则定义
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output_path=output_dir, # 输出文件夹
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frame_interval=interval, # 帧间隔（可根据实际视频帧率调整）
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)
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# 保存结束时间字符串
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time_data["end_time"] = str(datetime.now())
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with open(f"{output_dir}/time.json", "w") as f:
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f.write(json.dumps(time_data, ensure_ascii=False, indent=4))
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#================更新预览=================
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# 获取总帧数
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cap = cv2.VideoCapture(vid_path)
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total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
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cap.release()
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img_path, class_tag = update_preview(0, vid_name)
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return gr.update(maximum=total_frames-1, value=0, step = interval), img_path, class_tag
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# 创建 Gradio 页面
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with gr.Blocks() as demo:
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gr.Markdown("# 📸 隐患排查系统 (sam3 + qwen3.5-27b)")
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with gr.Row():
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with gr.Column():
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<<<<<<< HEAD
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initial_files: list[str] = load_file_list()
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default_video = initial_files[0] if initial_files else None
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vid_file = gr.Dropdown(
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label="视频",
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choices=initial_files,
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value=default_video, # 默认选中第一个
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interactive=True,
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)
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=======
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vid_name = gr.Textbox(label="视频名称", value="Miehhuoxqih")
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vid_end = gr.Textbox(label="视频后缀", value=".AVI")
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>>>>>>> 7562de4 (2 预览图还有点问题)
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run_sam3 = gr.Checkbox(label="1. 运行 SAM3 模型", value=True)
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run_inspection = gr.Checkbox(label="2. 运行隐患排查", value=True)
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gen_report = gr.Checkbox(label="3. 生成报告", value=True)
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audio_recognition = gr.Checkbox(label="4. 运行音频识别", value=False)
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run_button = gr.Button("运行", variant="primary")
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get_vid_path_btn = gr.Button("获取视频路径")
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full_path_text = gr.Textbox(visible=False)
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with gr.Column():
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preview = gr.Image(label="预览", scale=2)
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img_slider = gr.Slider(label="帧索引", minimum=0, maximum=0, value=0, step=1)
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textbox = gr.Textbox(label="隐患结果", lines=10)
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run_button.click(fn=run, inputs=[vid_file, run_sam3, run_inspection, gen_report], outputs=[img_slider, preview, textbox])
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img_slider.change(fn=update_preview, inputs=[img_slider, vid_file], outputs=[preview, textbox], show_progress="hidden")
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get_vid_path_btn.click(fn=get_full_vid_path, inputs=vid_file, outputs=full_path_text)
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def get_class_tag_by_frame(data, idx, fps):
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"""
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根据给定的帧索引 (idx)，返回在该帧范围内的所有对象的 class:tag 信息。
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参数:
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data (dict): 包含 'class', 'tag', 'objects' 的字典数据
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idx (int): 需要查询的帧索引
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返回:
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str: 符合条件的 class:tag 列表，多个结果之间用换行符分隔。如果没有匹配项，返回空字符串。
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"""
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# 参数检查
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if not isinstance(data, dict):
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raise ValueError("数据必须是字典类型")
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if 'class' not in data or 'tag' not in data or 'objects' not in data:
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raise ValueError("数据必须包含 'class', 'tag' 和 'objects' 键")
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class_list = data['class']
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tag_list = data['tag']
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objects = data['objects']
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interval = fps
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idx = int(idx / interval) #转换
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# 用于存储符合条件的 class:tag 字符串
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result = []
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all_class_tag = []
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# 遍历每个物体
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for obj in objects:
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# 根据 class_id 和 tag_id 获取对应的字符串
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class_str = class_list[obj['class_id']]
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tag_str = tag_list[obj['tag_id']]
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location = obj.get('location', '')
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# 检查帧范围是否包含 idx
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if obj['start_frame'] == idx:
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result.append(f"{class_str}:{tag_str} (位置: {location})")
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all_class_tag.append(f"{class_str}:{tag_str} (开始帧: {obj['start_frame']*interval}, 位置: {location})")
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# 使用换行符连接所有结果
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output = f"当前帧隐患：\n"+"\n".join(result)+"\n\n"+"所有对象的 class:tag 信息：\n"+"\n".join(all_class_tag)
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return output
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# 启动应用
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if __name__ == "__main__":
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demo.launch(
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debug=True,
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allowed_paths=[VIDEO_FOLDER]
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)
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