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