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LabelImg ascii报错

时间: 2024-08-14 11:09:41 浏览: 193
LabelImg是一款常用的图像标注工具,它通常用于创建训练数据集,尤其是用于机器学习任务中的图像分类和目标检测。ASCII报错可能是指在处理图像文件或转换过程中遇到了编码问题。当LabelImg尝试将图像信息转换成文本表示时,如果遇到无法识别的字符或者文件格式不符合预期,可能会出现ASCII错误。 这种错误可能是由于以下原因: 1. 图像文件格式不支持:LabelImg可能不支持某些特殊的图片格式,比如WebP等。请确认你正在使用的图片是常见的JPG、PNG或BMP格式。 2. 字符编码问题:如果图像中的文字包含非ASCII字符,而LabelImg默认的字符编码无法正确解析,就会出错。你可以检查图像是否包含非拉丁字母的文字,并尝试更改LabelImg的字符编码设置。 3. 图片损坏或不完整:损坏的图片可能导致LabelImg在读取时出现问题。请确保你的图片文件是完整的。 为了解决这个问题,你可以尝试以下步骤: 1. 检查并转换图片格式,如使用ImageMagick转换工具。 2. 确保图像文件内容清晰,无损,且字符可以正常显示。 3. 在LabelImg的配置中调整字符编码设置,特别是当你知道图片中可能存在非ASCII字符时。
相关问题

labelimg报错UnicodeEncodeError: 'ascii' codec can't encode characters in position 0-1: ordinal not in range

这个错误通常是由于在文件路径中包含非ASCII字符而导致的。解决此问题的一种方法是在打开文件时使用Python的codecs模块,该模块提供了一个更通用的open()函数,可以指定文件编码。以下是一个使用codecs模块打开文件的示例: ```python import codecs with codecs.open('文件路径', 'r', encoding='utf-8') as f: # 处理文件内容 ``` 如果您使用的是labelimg工具,可以尝试在打开文件时使用上述代码。如果仍然无法解决问题,请检查文件路径是否包含非ASCII字符,并尝试将其更改为ASCII字符。

labelimg报错:UnicodeEncodeError: 'ascii' codec can't encode characters in position 31-32: ordinal not in range(128)

UnicodeEncodeError通常发生在尝试使用ASCII编码方式对包含非ASCII字符的文本进行编码时。由于ASCII编码只支持128个字符,无法表示一些特殊的字符,如中文、日文等。在使用labelimg工具时遇到这个错误,可能是由于以下原因造成的: 1. 你可能在命令行界面中直接运行labelimg,并且指定了一个非UTF-8的编码环境。命令行界面和某些工具可能默认使用ASCII编码,当遇到非ASCII字符时就会抛出错误。 2. labelimg可能在其代码中有编码指定错误,或者它尝试打开一个包含非ASCII字符的文件时没有使用正确的编码方式。 为了解决这个问题,你可以尝试以下几个步骤: 1. 确保你的命令行界面使用的编码是UTF-8。在大多数现代操作系统中,你可以通过设置环境变量来指定命令行的默认编码为UTF-8。 2. 如果你是通过Python脚本运行labelimg,确保在脚本中正确处理了编码。例如,在脚本开始时添加以下代码来指定默认编码为UTF-8: ```python import sys import locale locale.setlocale(locale.LC_ALL, '') # 设置本地化选项,使用当前系统的默认设置 sys.stdout = open(sys.stdout.fileno(), mode='w', encoding='utf8', buffering=1) sys.stderr = open(sys.stderr.fileno(), mode='w', encoding='utf8', buffering=1) sys.stdin = open(sys.stdin.fileno(), mode='r', encoding='utf8', buffering=1) ``` 3. 如果labelimg是在图形用户界面(GUI)模式下运行出现错误,检查你的操作系统语言设置是否为UTF-8,并尝试更改系统语言或在运行labelimg之前设置环境变量。
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能否帮我修改代码让我避开或者解决这个报错# ======================================================= # 业务领域多模态RAG智能问答系统 (Business-RAG-MultiModal) # v2.1 - 最终稳定版 # ======================================================= # --- 核心依赖库导入 --- import os import hashlib import json import logging import base64 import pathlib import re import requests # 用于直接调用Ollama API from typing import List, Dict # --- LlamaIndex 核心导入 --- from llama_index.core import Settings from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, PromptTemplate, Document, StorageContext from llama_index.core.readers.base import BaseReader as LlamaBaseReader from llama_index.core.node_parser import SentenceSplitter from llama_index.core.schema import TextNode from llama_index.llms.ollama import Ollama from llama_index.core.postprocessor import SentenceTransformerRerank from llama_index.embeddings.huggingface import HuggingFaceEmbedding # --- Milvus 相关导入 --- from llama_index.vector_stores.milvus import MilvusVectorStore from pymilvus import utility, connections, Collection # --- 配置日志 --- logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') logger = logging.getLogger(__name__) # ======================================================= # 1. 全局配置区 # ======================================================= CONFIG = { "knowledge_base_dir": "knowledge_base", "image_cache_file": "image_description_cache_offline.json", "embed_model_path": "D:/models/text2vec-base-chinese", "reranker_model_path": "D:/models/bge-reranker-v2-m3", "llm_model_name": "qwen3:8b", "mllm_model_name": "llava", "llm_request_timeout": 600.0, "chunk_size": 512, "chunk_overlap": 50, "retrieval_top_k": 10, "rerank_top_n": 3, "device": "cpu", # --- Milvus配置 --- "milvus_host": "127.0.0.1", "milvus_port": "19530", "milvus_collection": "law_rag_collection_v1", "vector_dim": 768 } # ======================================================= # 2. 核心功能函数区 # ======================================================= def load_image_cache(): if os.path.exists(CONFIG["image_cache_file"]): with open(CONFIG["image_cache_file"], 'r', encoding='utf-8') as f: try: return json.load(f) except json.JSONDecodeError: return {} return {} def save_image_cache(cache): with open(CONFIG["image_cache_file"], 'w', encoding='utf-8') as f: json.dump(cache, f, ensure_ascii=False, indent=4) def get_image_description_from_mllm(image_bytes: bytes, local_mllm: Ollama, question="请详细描述这张图片的内容,如果图片中有文字也请一并识别出来。") -> str: """ 【修正版】直接调用Ollama API来获取图片描述,并使用正确的sha256。 """ image_cache = load_image_cache() image_hash = hashlib.sha256(image_bytes).hexdigest() if image_hash in image_cache: logger.info(f" - 发现已缓存的离线图片描述 (hash: {image_hash[:8]}...),从缓存加载。") return image_cache[image_hash] logger.info(f" - 未找到图片缓存,正在直接调用Ollama API (模型: {local_mllm.model})...") try: image_b64 = base64.b64encode(image_bytes).decode("utf-8") payload = { "model": local_mllm.model, "prompt": question, "images": [image_b64], "stream": False } response = requests.post( "http://localhost:11434/api/generate", json=payload, timeout=CONFIG["llm_request_timeout"] ) response.raise_for_status() response_data = response.json() description = response_data.get('response', '[模型未返回有效描述]').strip() formatted_description = f"[图片描述]: {description}\n" image_cache[image_hash] = formatted_description save_image_cache(image_cache) return formatted_description except requests.exceptions.RequestException as e: logger.error(f"直接调用Ollama API处理图片时发生网络异常: {e}") return "[网络异常导致图片处理失败]\n" except Exception as e: logger.error(f"处理图片时发生未知异常: {e}") return "[未知异常导致图片处理失败]\n" class CustomMultimodalReader(LlamaBaseReader): def __init__(self, mllm_instance: Ollama): super().__init__() self.mllm = mllm_instance def load_data(self, file_path_obj: pathlib.Path, extra_info: Dict = None) -> List[Document]: file_path_str = str(file_path_obj) if file_path_str.endswith(".pdf"): return self._load_pdf(file_path_str, extra_info) elif file_path_str.endswith(".docx"): return self._load_docx(file_path_str, extra_info) else: # 为 .txt 文件添加一个基本的加载器 try: with open(file_path_str, 'r', encoding='utf-8') as f: text = f.read() return [Document(text=text, extra_info={**(extra_info or {}), "file_name": os.path.basename(file_path_str)})] except Exception as e: logger.error(f"处理TXT文件 '{file_path_str}' 时发生错误: {e}") return [] def _load_pdf(self, file_path: str, extra_info: Dict = None) -> List[Document]: documents = [] try: import pypdf with open(file_path, "rb") as fp: reader = pypdf.PdfReader(fp) if reader.is_encrypted: logger.warning(f"文件 {os.path.basename(file_path)} 是加密PDF,已跳过。") return [] for i, page in enumerate(reader.pages): page_info = {**(extra_info or {}), "page_label": str(i + 1), "file_name": os.path.basename(file_path)} if page_text := page.extract_text(): documents.append(Document(text=page_text.strip(), extra_info=page_info.copy())) for img_file_obj in page.images: if img_bytes := img_file_obj.data: image_description = get_image_description_from_mllm(img_bytes, self.mllm) documents.append(Document(text=image_description, extra_info={**page_info.copy(), "content_type": "image_description"})) except Exception as e: logger.error(f"处理PDF文件 '{file_path}' 时发生错误: {e}") return documents def _load_docx(self, file_path: str, extra_info: Dict = None) -> List[Document]: documents = [] try: import docx doc = docx.Document(file_path) file_info = {**(extra_info or {}), "file_name": os.path.basename(file_path)} for para in doc.paragraphs: if para.text.strip(): documents.append(Document(text=para.text.strip(), extra_info=file_info.copy())) for table in doc.tables: table_text = "\n".join([" | ".join([cell.text for cell in row.cells]) for row in table.rows]).strip() if table_text: documents.append(Document(text=f"[表格内容]:\n{table_text}", extra_info={**file_info.copy(), "content_type": "table_content"})) for rel in doc.part.rels.values(): if "image" in rel.target_ref: if image_bytes := rel.target_part.blob: image_description = get_image_description_from_mllm(image_bytes, self.mllm) documents.append(Document(text=image_description, extra_info={**file_info.copy(), "content_type": "image_description"})) except Exception as e: logger.error(f"处理DOCX文件 '{file_path}' 时发生错误: {e}") return documents # --- RAG核心流程函数 --- def setup_models_and_services(): """集中加载所有本地AI模型,并进行全局配置。""" logger.info("--- 步骤A: 加载所有本地AI模型 ---") embed_model = HuggingFaceEmbedding( model_name=CONFIG["embed_model_path"], device=CONFIG["device"] ) logger.info(f"成功加载本地嵌入模型: {CONFIG['embed_model_path']}") llm_model = Ollama(model=CONFIG["llm_model_name"], request_timeout=CONFIG["llm_request_timeout"]) logger.info(f"成功配置本地Ollama LLM (模型: {CONFIG['llm_model_name']})") mllm_for_parsing = Ollama(model=CONFIG["mllm_model_name"], request_timeout=300.0) logger.info(f"成功配置本地Ollama MLLM (模型: {CONFIG['mllm_model_name']})") reranker = SentenceTransformerRerank( model=CONFIG["reranker_model_path"], top_n=CONFIG["rerank_top_n"], device=CONFIG["device"] ) logger.info(f"成功加载本地重排模型: {CONFIG['reranker_model_path']}") # 将加载好的模型设置到全局 Settings 中,确保所有组件统一使用 Settings.embed_model = embed_model Settings.llm = llm_model logger.info("--- 已将embed_model和llm配置为全局默认 ---") logger.info("--- 所有AI模型加载完成 ---") return llm_model, embed_model, reranker, mllm_for_parsing def build_knowledge_index(mllm_for_parsing: Ollama, embed_model: HuggingFaceEmbedding): """ 【最终修正版】构建向量索引并将其持久化到Milvus数据库。 包含手动嵌入生成以绕过库的潜在bug。 """ logger.info("--- 步骤B: 连接Milvus并构建/加载知识库向量索引 ---") try: connections.connect(alias="default", host=CONFIG["milvus_host"], port=CONFIG["milvus_port"]) logger.info(f"成功连接到 Milvus 服务 at {CONFIG['milvus_host']}:{CONFIG['milvus_port']}") except Exception as e: logger.error(f"无法连接到 Milvus 服务: {e}") raise vector_store = MilvusVectorStore( uri=f"http://{CONFIG['milvus_host']}:{CONFIG['milvus_port']}", collection_name=CONFIG["milvus_collection"], dim=CONFIG["vector_dim"], overwrite=False ) collection_exists_and_has_content = False if utility.has_collection(CONFIG["milvus_collection"]): collection = Collection(name=CONFIG["milvus_collection"]) collection.load() if collection.num_entities > 0: collection_exists_and_has_content = True if collection_exists_and_has_content: logger.info(f"在Milvus中已找到包含实体的集合,直接加载索引...") index = VectorStoreIndex.from_vector_store(vector_store) logger.info("从Milvus加载索引完成。") else: if utility.has_collection(CONFIG["milvus_collection"]): logger.info("在Milvus中找到空集合,开始处理并填充数据...") else: logger.info(f"在Milvus中未找到集合,开始完整的数据处理和索引构建流程...") # 步骤 1: 数据加载和切分 (此部分不变) reader = SimpleDirectoryReader( input_dir=CONFIG["knowledge_base_dir"], required_exts=[".pdf", ".docx", ".txt"], file_extractor={".pdf": CustomMultimodalReader(mllm_instance=mllm_for_parsing), ".docx": CustomMultimodalReader(mllm_instance=mllm_for_parsing)}, recursive=True ) documents = reader.load_data(show_progress=True) all_nodes = [] sentence_splitter = SentenceSplitter(chunk_size=CONFIG["chunk_size"], chunk_overlap=CONFIG["chunk_overlap"]) for doc in documents: filename = doc.metadata.get("file_name", "").lower() if doc.metadata.get("content_type") == "image_description": all_nodes.append(doc); continue if filename.endswith(".pdf"): article_pattern = r'(第[一二三四五六七八九十百千万零〇\d]+条)'; text_chunks = re.split(article_pattern, doc.text); i = 1 while i < len(text_chunks): article_title = text_chunks[i]; article_content = text_chunks[i+1] if (i + 1) < len(text_chunks) else "" full_article_text = (article_title + article_content).strip() if full_article_text: node = Document(text=full_article_text, extra_info=doc.metadata.copy()); all_nodes.append(node) i += 2 else: nodes = sentence_splitter.get_nodes_from_documents([doc]); all_nodes.extend(nodes) logger.info(f"文档条件化切分完毕,共生成 {len(all_nodes)} 个内容块 (Nodes)。") # --- 【核心修正】 --- # 步骤 2: 手动、显式地为所有节点生成向量嵌入 logger.info(f"正在为 {len(all_nodes)} 个节点手动生成向量嵌入...") for node in all_nodes: # node.get_content() 是获取节点文本最稳健的方法 node.embedding = embed_model.get_text_embedding(node.get_content()) logger.info("所有节点的向量嵌入已手动生成。") # --- 【核心修正结束】 --- # 步骤 3: 将已经带有向量的节点添加到Milvus logger.info(f"正在将 {len(all_nodes)} 个带有预生成向量的节点添加到Milvus...") vector_store.add(all_nodes) logger.info("节点已成功添加到Milvus。") # 步骤 4: 从已填充的向量存储创建索引对象 index = VectorStoreIndex.from_vector_store(vector_store) logger.info("索引对象创建完成。") connections.disconnect("default") logger.info("已断开与 Milvus 服务的连接。") return index def run_query_pipeline(index: VectorStoreIndex, llm_model: Ollama, reranker: SentenceTransformerRerank): """启动问答流程,循环处理预设的问题。""" logger.info("--- 步骤C: 开始RAG问答流程 ---") QA_PROMPT_TEMPLATE = PromptTemplate( "你是一个专业的业务问答助手,负责根据内部知识库提供精准、可靠的回答。\n\n" "**你的任务是:**\n" "1. 仔细阅读下面提供的“参考信息”。\n" "2. 根据“参考信息”直接回答“用户问题”,禁止进行任何形式的猜测、推理或使用你自己的知识。\n" "3. **引用来源**:在回答中,如果引用了某份文件的内容,必须在相关句子末尾用 (文件名) 的格式注明来源。\n" "4. **版本对比**:如果参考信息来自不同版本的文件(例如,文件名中包含年份),请对比说明它们之间的差异。\n" "5. **提供建议**:在回答的最后,根据回答内容,提供1-2条具体、可执行的业务建议。\n" "6. **未知问题**:如果“参考信息”中完全没有能回答问题的内容,你必须且只能回答:“根据提供的资料,无法回答该问题。”\n" "7. **格式要求**:回答的最后,必须附上一个“参考依据”列表,列出所有被引用的文件名。\n\n" "---------------------\n" "**参考信息:**\n{context_str}\n" "---------------------\n" "**用户问题:** {query_str}\n\n" "**你的回答:**\n" ) query_engine = index.as_query_engine( similarity_top_k=CONFIG["retrieval_top_k"], node_postprocessors=[reranker], text_qa_template=QA_PROMPT_TEMPLATE # llm会从全局Settings获取 ) questions = [ "根据附图1的技术架构图,究竟是哪个芯片独立负责生成刷新信号?", "数据出境安全评估申报流程图里,如果个人信息达到10万人规模该怎么办?", "我国公民的基本权力以及义务有哪些", "借钱不还怎么办?" ] for q in questions: logger.info(f"\n{'='*70}\n--- 用户提问: {q} ---") try: response = query_engine.query(q) logger.info("\n--- 模型最终回答 ---\n" + str(response)) logger.info("\n--- 回答引用的参考信息 (经重排后) ---") for i, node_with_score in enumerate(response.source_nodes): logger.info(f"--- 来源 {i+1} (得分: {node_with_score.score:.4f}, 文件: {node_with_score.metadata.get('file_name', 'N/A')}) ---") node = node_with_score.node if hasattr(node, 'text') and node.text: logger.info(f"内容预览: {node.text[:150]}...\n") else: logger.info(f"内容预览: [这是一个非文本节点,类型为: {type(node).__name__}]\n") except Exception as e: logger.error(f"执行RAG查询时发生错误: {e}", exc_info=True) logger.info(f"{'='*70}") # ======================================================= # 3. 主执行入口 # ======================================================= if __name__ == "__main__": logger.info("===== 启动业务领域多模态RAG智能问答系统 =====") try: llm, embed_model, reranker, mllm = setup_models_and_services() knowledge_index = build_knowledge_index(mllm, embed_model) run_query_pipeline(knowledge_index, llm, reranker) except Exception as e: logger.error(f"程序主流程发生致命错误,即将退出: {e}", exc_info=True) exit(1) logger.info("\n===== RAG系统执行完成 ====="),(rag_project_env) PS D:\new_rag> docker ps >> (rag_project_env) PS D:\new_rag> & D:/miniconda/envs/rag_project_env/python.exe d:/new_rag/law_rag.py 2025-07-24 09:54:07,238 - INFO - ===== 启动业务领域多模态RAG智能问答系统 ===== 2025-07-24 09:54:07,238 - INFO - --- 步骤A: 加载所有本地AI模型 --- 2025-07-24 09:54:07,240 - INFO - Load pretrained SentenceTransformer: D:/models/text2vec-base-chinese 2025-07-24 09:54:07,958 - INFO - 成功加载本地嵌入模型: D:/models/text2vec-base-chinese 2025-07-24 09:54:07,958 - INFO - 成功配置本地Ollama LLM (模型: qwen3:8b) 2025-07-24 09:54:07,958 - INFO - 成功配置本地Ollama MLLM (模型: llava) 2025-07-24 09:54:08,410 - INFO - 成功加载本地重排模型: D:/models/bge-reranker-v2-m3 2025-07-24 09:54:08,410 - INFO - --- 已将embed_model和llm配置为全局默认 --- 2025-07-24 09:54:08,410 - INFO - --- 所有AI模型加载完成 --- 2025-07-24 09:54:08,410 - INFO - --- 步骤B: 连接Milvus并构建/加载知识库向量索引 --- 2025-07-24 09:54:18,468 - ERROR - 无法连接到 Milvus 服务: <MilvusException: (code=2, message=Fail connecting to server on 127.0.0.1:19530, illegal connection params or server unavailable)> 2025-07-24 09:54:18,468 - ERROR - 程序主流程发生致命错误,即将退出: <MilvusException: (code=2, message=Fail connecting to server on 127.0.0.1:19530, illegal connection params or server unavailable)> Traceback (most recent call last): File "d:\new_rag\law_rag.py", line 357, in <module> knowledge_index = build_knowledge_index(mllm, embed_model) File "d:\new_rag\law_rag.py", line 223, in build_knowledge_index connections.connect(alias="default", host=CONFIG["milvus_host"], port=CONFIG["milvus_port"]) File "D:\miniconda\envs\rag_project_env\lib\site-packages\pymilvus\orm\connections.py", line 459, in connect connect_milvus(**kwargs, user=user, password=password, token=token, db_name=db_name) File "D:\miniconda\envs\rag_project_env\lib\site-packages\pymilvus\orm\connections.py", line 420, in connect_milvus raise e from e File "D:\miniconda\envs\rag_project_env\lib\site-packages\pymilvus\orm\connections.py", line 412, in connect_milvus gh._wait_for_channel_ready(timeout=timeout) File "D:\miniconda\envs\rag_project_env\lib\site-packages\pymilvus\client\grpc_handler.py", line 159, in _wait_for_channel_ready raise MilvusException( pymilvus.exceptions.MilvusException: <MilvusException: (code=2, message=Fail connecting to server on 127.0.0.1:19530, illegal connection params or server unavailable)>,services: etcd: container_name: milvus-etcd image: quay.io/coreos/etcd:v3.5.5 environment: - ETCD_AUTO_COMPACTION_MODE=revision - ETCD_AUTO_COMPACTION_RETENTION=1000 - ETCD_QUOTA_BACKEND_BYTES=4294967296 - ETCD_SNAPSHOT_COUNT=50000 volumes: - ./volumes/etcd:/etcd command: etcd -advertise-client-urls=http://127.0.0.1:2379 -listen-client-urls http://0.0.0.0:2379 --data-dir /etcd minio: container_name: milvus-minio image: quay.io/minio/minio:RELEASE.2023-03-20T20-16-18Z environment: - MINIO_ROOT_USER=minioadmin - MINIO_ROOT_PASSWORD=minioadmin volumes: - ./volumes/minio:/minio_data command: minio server /minio_data standalone: container_name: milvus-standalone image: milvusdb/milvus:v2.5.0 # <-- 修正点在这里 command: ["milvus", "run", "standalone"] environment: - ETCD_ENDPOINTS=etcd:2379 - MINIO_ADDRESS=minio:9000 volumes: - ./volumes/milvus:/var/lib/milvus ports: - "19530:19530" # Milvus port - "9091:9091" # Milvus metrics port depends_on: - "etcd" - "minio" volumes: etcd: minio: milvus:

# ======================================================= # 业务领域多模态RAG智能问答系统 (Business-RAG-MultiModal) # v2.1 - 最终稳定版 # ======================================================= # --- 核心依赖库导入 --- import os import hashlib import json import logging import base64 import pathlib import re import requests # 用于直接调用Ollama API import time # 新增:用于重试机制 from typing import List, Dict # --- LlamaIndex 核心导入 --- from llama_index.core import Settings from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, PromptTemplate, Document, StorageContext from llama_index.core.readers.base import BaseReader as LlamaBaseReader from llama_index.core.node_parser import SentenceSplitter from llama_index.core.schema import TextNode from llama_index.llms.ollama import Ollama from llama_index.core.postprocessor import SentenceTransformerRerank from llama_index.embeddings.huggingface import HuggingFaceEmbedding # --- Milvus 相关导入 --- from llama_index.vector_stores.milvus import MilvusVectorStore from pymilvus import utility, connections, Collection # --- 配置日志 --- logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') logger = logging.getLogger(__name__) # ======================================================= # 1. 全局配置区 # ======================================================= CONFIG = { "knowledge_base_dir": "knowledge_base", "image_cache_file": "image_description_cache_offline.json", "embed_model_path": "D:/models/text2vec-base-chinese", "reranker_model_path": "D:/models/bge-reranker-v2-m3", "llm_model_name": "qwen3:8b", "mllm_model_name": "llava", "llm_request_timeout": 600.0, "chunk_size": 512, "chunk_overlap": 50, "retrieval_top_k": 10, "rerank_top_n": 3, "device": "cpu", # --- Milvus配置 --- "milvus_host": "127.0.0.1", # 确保与Docker容器端口一致 "milvus_port": "19530", # 默认端口 "milvus_collection": "law_rag_collection_v1", "vector_dim": 768 } # ======================================================= # 2. 核心功能函数区 # ======================================================= def load_image_cache(): if os.path.exists(CONFIG["image_cache_file"]): with open(CONFIG["image_cache_file"], 'r', encoding='utf-8') as f: try: return json.load(f) except json.JSONDecodeError: return {} return {} def save_image_cache(cache): with open(CONFIG["image_cache_file"], 'w', encoding='utf-8') as f: json.dump(cache, f, ensure_ascii=False, indent=4) def get_image_description_from_mllm(image_bytes: bytes, local_mllm: Ollama, question="请详细描述这张图片的内容,如果图片中有文字也请一并识别出来。") -> str: """ 【修正版】直接调用Ollama API来获取图片描述,并使用正确的sha256。 """ image_cache = load_image_cache() image_hash = hashlib.sha256(image_bytes).hexdigest() if image_hash in image_cache: logger.info(f" - 发现已缓存的离线图片描述 (hash: {image_hash[:8]}...),从缓存加载。") return image_cache[image_hash] logger.info(f" - 未找到图片缓存,正在直接调用Ollama API (模型: {local_mllm.model})...") try: image_b64 = base64.b64encode(image_bytes).decode("utf-8") payload = { "model": local_mllm.model, "prompt": question, "images": [image_b64], "stream": False } response = requests.post( "http://localhost:11434/api/generate", json=payload, timeout=CONFIG["llm_request_timeout"] ) response.raise_for_status() response_data = response.json() description = response_data.get('response', '[模型未返回有效描述]').strip() formatted_description = f"[图片描述]: {description}\n" image_cache[image_hash] = formatted_description save_image_cache(image_cache) return formatted_description except requests.exceptions.RequestException as e: logger.error(f"直接调用Ollama API处理图片时发生网络异常: {e}") return "[网络异常导致图片处理失败]\n" except Exception as e: logger.error(f"处理图片时发生未知异常: {e}") return "[未知异常导致图片处理失败]\n" class CustomMultimodalReader(LlamaBaseReader): def __init__(self, mllm_instance: Ollama): super().__init__() self.mllm = mllm_instance def load_data(self, file_path_obj: pathlib.Path, extra_info: Dict = None) -> List[Document]: file_path_str = str(file_path_obj) if file_path_str.endswith(".pdf"): return self._load_pdf(file_path_str, extra_info) elif file_path_str.endswith(".docx"): return self._load_docx(file_path_str, extra_info) else: # 为 .txt 文件添加一个基本的加载器 try: with open(file_path_str, 'r', encoding='utf-8') as f: text = f.read() return [Document(text=text, extra_info={**(extra_info or {}), "file_name": os.path.basename(file_path_str)})] except Exception as e: logger.error(f"处理TXT文件 '{file_path_str}' 时发生错误: {e}") return [] def _load_pdf(self, file_path: str, extra_info: Dict = None) -> List[Document]: documents = [] try: import pypdf with open(file_path, "rb") as fp: reader = pypdf.PdfReader(fp) if reader.is_encrypted: logger.warning(f"文件 {os.path.basename(file_path)} 是加密PDF,已跳过。") return [] for i, page in enumerate(reader.pages): page_info = {**(extra_info or {}), "page_label": str(i + 1), "file_name": os.path.basename(file_path)} if page_text := page.extract_text(): documents.append(Document(text=page_text.strip(), extra_info=page_info.copy())) for img_file_obj in page.images: if img_bytes := img_file_obj.data: image_description = get_image_description_from_mllm(img_bytes, self.mllm) documents.append(Document(text=image_description, extra_info={**page_info.copy(), "content_type": "image_description"})) except Exception as e: logger.error(f"处理PDF文件 '{file_path}' 时发生错误: {e}") return documents def _load_docx(self, file_path: str, extra_info: Dict = None) -> List[Document]: documents = [] try: import docx doc = docx.Document(file_path) file_info = {**(extra_info or {}), "file_name": os.path.basename(file_path)} for para in doc.paragraphs: if para.text.strip(): documents.append(Document(text=para.text.strip(), extra_info=file_info.copy())) for table in doc.tables: table_text = "\n".join([" | ".join([cell.text for cell in row.cells]) for row in table.rows]).strip() if table_text: documents.append(Document(text=f"[表格内容]:\n{table_text}", extra_info={**file_info.copy(), "content_type": "table_content"})) for rel in doc.part.rels.values(): if "image" in rel.target_ref: if image_bytes := rel.target_part.blob: image_description = get_image_description_from_mllm(image_bytes, self.mllm) documents.append(Document(text=image_description, extra_info={**file_info.copy(), "content_type": "image_description"})) except Exception as e: logger.error(f"处理DOCX文件 '{file_path}' 时发生错误: {e}") return documents # --- RAG核心流程函数 --- def setup_models_and_services(): """集中加载所有本地AI模型,并进行全局配置。""" logger.info("--- 步骤A: 加载所有本地AI模型 ---") embed_model = HuggingFaceEmbedding( model_name=CONFIG["embed_model_path"], device=CONFIG["device"] ) logger.info(f"成功加载本地嵌入模型: {CONFIG['embed_model_path']}") llm_model = Ollama(model=CONFIG["llm_model_name"], request_timeout=CONFIG["llm_request_timeout"]) logger.info(f"成功配置本地Ollama LLM (模型: {CONFIG['llm_model_name']})") mllm_for_parsing = Ollama(model=CONFIG["mllm_model_name"], request_timeout=300.0) logger.info(f"成功配置本地Ollama MLLM (模型: {CONFIG['mllm_model_name']})") reranker = SentenceTransformerRerank( model=CONFIG["reranker_model_path"], top_n=CONFIG["rerank_top_n"], device=CONFIG["device"] ) logger.info(f"成功加载本地重排模型: {CONFIG['reranker_model_path']}") # 将加载好的模型设置到全局 Settings 中,确保所有组件统一使用 Settings.embed_model = embed_model Settings.llm = llm_model logger.info("--- 已将embed_model和llm配置为全局默认 ---") logger.info("--- 所有AI模型加载完成 ---") return llm_model, embed_model, reranker, mllm_for_parsing def build_knowledge_index(mllm_for_parsing: Ollama, embed_model: HuggingFaceEmbedding): """ 【最终修正版】构建向量索引并将其持久化到Milvus数据库。 包含手动嵌入生成以绕过库的潜在bug。 """ logger.info("--- 步骤B: 连接Milvus并构建/加载知识库向量索引 ---") # ===== 新增:Milvus连接重试机制 ===== max_retries = 5 retry_delay = 5 # 秒 connected = False for attempt in range(max_retries): try: connections.connect(alias="default", host=CONFIG["milvus_host"], port=CONFIG["milvus_port"]) logger.info(f"成功连接到 Milvus 服务 at {CONFIG['milvus_host']}:{CONFIG['milvus_port']}") connected = True break except Exception as e: logger.warning(f"连接Milvus失败(尝试 {attempt+1}/{max_retries}): {e}") if attempt < max_retries - 1: logger.info(f"{retry_delay}秒后重试...") time.sleep(retry_delay) else: logger.error(f"无法连接到 Milvus 服务,已达到最大重试次数") raise ConnectionError(f"无法连接Milvus: {str(e)}") if not connected: raise RuntimeError("Milvus连接失败") vector_store = MilvusVectorStore( uri=f"http://{CONFIG['milvus_host']}:{CONFIG['milvus_port']}", collection_name=CONFIG["milvus_collection"], dim=CONFIG["vector_dim"], overwrite=False ) collection_exists_and_has_content = False if utility.has_collection(CONFIG["milvus_collection"]): collection = Collection(name=CONFIG["milvus_collection"]) collection.load() if collection.num_entities > 0: collection_exists_and_has_content = True if collection_exists_and_has_content: logger.info(f"在Milvus中已找到包含实体的集合,直接加载索引...") index = VectorStoreIndex.from_vector_store(vector_store) logger.info("从Milvus加载索引完成。") else: if utility.has_collection(CONFIG["milvus_collection"]): logger.info("在Milvus中找到空集合,开始处理并填充数据...") else: logger.info(f"在Milvus中未找到集合,开始完整的数据处理和索引构建流程...") # 步骤 1: 数据加载和切分 (此部分不变) reader = SimpleDirectoryReader( input_dir=CONFIG["knowledge_base_dir"], required_exts=[".pdf", ".docx", ".txt"], file_extractor={".pdf": CustomMultimodalReader(mllm_instance=mllm_for_parsing), ".docx": CustomMultimodalReader(mllm_instance=mllm_for_parsing)}, recursive=True ) documents = reader.load_data(show_progress=True) all_nodes = [] sentence_splitter = SentenceSplitter(chunk_size=CONFIG["chunk_size"], chunk_overlap=CONFIG["chunk_overlap"]) for doc in documents: filename = doc.metadata.get("file_name", "").lower() if doc.metadata.get("content_type") == "image_description": all_nodes.append(doc); continue if filename.endswith(".pdf"): article_pattern = r'(第[一二三四五六七八九十百千万零〇\d]+条)'; text_chunks = re.split(article_pattern, doc.text); i = 1 while i < len(text_chunks): article_title = text_chunks[i]; article_content = text_chunks[i+1] if (i + 1) < len(text_chunks) else "" full_article_text = (article_title + article_content).strip() if full_article_text: node = Document(text=full_article_text, extra_info=doc.metadata.copy()); all_nodes.append(node) i += 2 else: nodes = sentence_splitter.get_nodes_from_documents([doc]); all_nodes.extend(nodes) logger.info(f"文档条件化切分完毕,共生成 {len(all_nodes)} 个内容块 (Nodes)。") # --- 【核心修正】 --- # 步骤 2: 手动、显式地为所有节点生成向量嵌入 logger.info(f"正在为 {len(all_nodes)} 个节点手动生成向量嵌入...") for node in all_nodes: # node.get_content() 是获取节点文本最稳健的方法 node.embedding = embed_model.get_text_embedding(node.get_content()) logger.info("所有节点的向量嵌入已手动生成。") # --- 【核心修正结束】 --- # 步骤 3: 将已经带有向量的节点添加到Milvus logger.info(f"正在将 {len(all_nodes)} 个带有预生成向量的节点添加到Milvus...") vector_store.add(all_nodes) logger.info("节点已成功添加到Milvus。") # 步骤 4: 从已填充的向量存储创建索引对象 index = VectorStoreIndex.from_vector_store(vector_store) logger.info("索引对象创建完成。") connections.disconnect("default") logger.info("已断开与 Milvus 服务的连接。") return index def run_query_pipeline(index: VectorStoreIndex, llm_model: Ollama, reranker: SentenceTransformerRerank): """启动问答流程,循环处理预设的问题。""" logger.info("--- 步骤C: 开始RAG问答流程 ---") QA_PROMPT_TEMPLATE = PromptTemplate( "你是一个专业的业务问答助手,负责根据内部知识库提供精准、可靠的回答。\n\n" "**你的任务是:**\n" "1. 仔细阅读下面提供的“参考信息”。\n" "2. 根据“参考信息”直接回答“用户问题”,禁止进行任何形式的猜测、推理或使用你自己的知识。\n" "3. **引用来源**:在回答中,如果引用了某份文件的内容,必须在相关句子末尾用 (文件名) 的格式注明来源。\n" "4. **版本对比**:如果参考信息来自不同版本的文件(例如,文件名中包含年份),请对比说明它们之间的差异。\n" "5. **提供建议**:在回答的最后,根据回答内容,提供1-2条具体、可执行的业务建议。\n" "6. **未知问题**:如果“参考信息”中完全没有能回答问题的内容,你必须且只能回答:“根据提供的资料,无法回答该问题。”\n" "7. **格式要求**:回答的最后,必须附上一个“参考依据”列表,列出所有被引用的文件名。\n\n" "---------------------\n" "**参考信息:**\n{context_str}\n" "---------------------\n" "**用户问题:** {query_str}\n\n" "**你的回答:**\n" ) query_engine = index.as_query_engine( similarity_top_k=CONFIG["retrieval_top_k"], node_postprocessors=[reranker], text_qa_template=QA_PROMPT_TEMPLATE # llm会从全局Settings获取 ) questions = [ "根据附图1的技术架构图,究竟是哪个芯片独立负责生成刷新信号?", "数据出境安全评估申报流程图里,如果个人信息达到10万人规模该怎么办?", "我国公民的基本权力以及义务有哪些", "借钱不还怎么办?" ] for q in questions: logger.info(f"\n{'='*70}\n--- 用户提问: {q} ---") try: response = query_engine.query(q) logger.info("\n--- 模型最终回答 ---\n" + str(response)) logger.info("\n--- 回答引用的参考信息 (经重排后) ---") for i, node_with_score in enumerate(response.source_nodes): logger.info(f"--- 来源 {i+1} (得分: {node_with_score.score:.4f}, 文件: {node_with_score.metadata.get('file_name', 'N/A')}) ---") node = node_with_score.node if hasattr(node, 'text') and node.text: logger.info(f"内容预览: {node.text[:150]}...\n") else: logger.info(f"内容预览: [这是一个非文本节点,类型为: {type(node).__name__}]\n") except Exception as e: logger.error(f"执行RAG查询时发生错误: {e}", exc_info=True) logger.info(f"{'='*70}") # ======================================================= # 3. 主执行入口 # ======================================================= if __name__ == "__main__": logger.info("===== 启动业务领域多模态RAG智能问答系统 =====") try: llm, embed_model, reranker, mllm = setup_models_and_services() knowledge_index = build_knowledge_index(mllm, embed_model) run_query_pipeline(knowledge_index, llm, reranker) except Exception as e: logger.error(f"程序主流程发生致命错误,即将退出: {e}", exc_info=True) exit(1) logger.info("\n===== RAG系统执行完成 =====") 这是我的代码,(rag_project_env) PS D:\new_rag> & D:/miniconda/envs/rag_project_env/python.exe d:/new_rag/law_rag.py 2025-07-24 11:26:07,150 - INFO - ===== 启动业务领域多模态RAG智能问答系统 ===== 2025-07-24 11:26:07,150 - INFO - --- 步骤A: 加载所有本地AI模型 --- 2025-07-24 11:26:07,153 - INFO - Load pretrained SentenceTransformer: D:/models/text2vec-base-chinese 2025-07-24 11:26:08,791 - INFO - 成功加载本地嵌入模型: D:/models/text2vec-base-chinese 2025-07-24 11:26:08,791 - INFO - 成功配置本地Ollama LLM (模型: qwen3:8b) 2025-07-24 11:26:08,791 - INFO - 成功配置本地Ollama MLLM (模型: llava) 2025-07-24 11:26:09,586 - INFO - 成功加载本地重排模型: D:/models/bge-reranker-v2-m3 2025-07-24 11:26:09,586 - INFO - --- 已将embed_model和llm配置为全局默认 --- 2025-07-24 11:26:09,586 - INFO - --- 所有AI模型加载完成 --- 2025-07-24 11:26:09,586 - INFO - --- 步骤B: 连接Milvus并构建/加载知识库向量索引 --- 2025-07-24 11:26:19,712 - WARNING - 连接Milvus失败(尝试 1/5): <MilvusException: (code=2, message=Fail connecting to server on 127.0.0.1:19530, illegal connection params or server unavailable)> 2025-07-24 11:26:19,712 - INFO - 5秒后重试... 2025-07-24 11:26:34,849 - WARNING - 连接Milvus失败(尝试 2/5): <MilvusException: (code=2, message=Fail connecting to server on 127.0.0.1:19530, illegal connection params or server unavailable)> 2025-07-24 11:26:34,849 - INFO - 5秒后重试... 2025-07-24 11:26:49,974 - WARNING - 连接Milvus失败(尝试 3/5): <MilvusException: (code=2, message=Fail connecting to server on 127.0.0.1:19530, illegal connection params or server unavailable)> 2025-07-24 11:26:49,978 - INFO - 5秒后重试... 2025-07-24 11:27:05,102 - WARNING - 连接Milvus失败(尝试 4/5): <MilvusException: (code=2, message=Fail connecting to server on 127.0.0.1:19530, illegal connection params or server unavailable)> 2025-07-24 11:27:05,102 - INFO - 5秒后重试... 2025-07-24 11:27:20,192 - WARNING - 连接Milvus失败(尝试 5/5): <MilvusException: (code=2, message=Fail connecting to server on 127.0.0.1:19530, illegal connection params or server unavailable)> 2025-07-24 11:27:20,192 - ERROR - 无法连接到 Milvus 服务,已达到最大重试次数 2025-07-24 11:27:20,192 - ERROR - 程序主流程发生致命错误,即将退出: 无法连接Milvus: <MilvusException: (code=2, message=Fail connecting to server on 127.0.0.1:19530, illegal connection params or server unavailable)> Traceback (most recent call last): File "d:\new_rag\law_rag.py", line 230, in build_knowledge_index connections.connect(alias="default", host=CONFIG["milvus_host"], port=CONFIG["milvus_port"]) File "D:\miniconda\envs\rag_project_env\lib\site-packages\pymilvus\orm\connections.py", line 459, in connect connect_milvus(**kwargs, user=user, password=password, token=token, db_name=db_name) File "D:\miniconda\envs\rag_project_env\lib\site-packages\pymilvus\orm\connections.py", line 420, in connect_milvus raise e from e File "D:\miniconda\envs\rag_project_env\lib\site-packages\pymilvus\orm\connections.py", line 412, in connect_milvus gh._wait_for_channel_ready(timeout=timeout) File "D:\miniconda\envs\rag_project_env\lib\site-packages\pymilvus\client\grpc_handler.py", line 159, in _wait_for_channel_ready raise MilvusException( pymilvus.exceptions.MilvusException: <MilvusException: (code=2, message=Fail connecting to server on 127.0.0.1:19530, illegal connection params or server unavailable)> During handling of the above exception, another exception occurred: Traceback (most recent call last): File "d:\new_rag\law_rag.py", line 374, in <module> knowledge_index = build_knowledge_index(mllm, embed_model) File "d:\new_rag\law_rag.py", line 241, in build_knowledge_index raise ConnectionError(f"无法连接Milvus: {str(e)}") ConnectionError: 无法连接Milvus: <MilvusException: (code=2, message=Fail connecting to server on 127.0.0.1:19530, illegal connection params or server unavailable)>这是我的报错,services: etcd: container_name: milvus-etcd image: quay.io/coreos/etcd:v3.5.5 environment: - ETCD_AUTO_COMPACTION_MODE=revision - ETCD_AUTO_COMPACTION_RETENTION=1000 - ETCD_QUOTA_BACKEND_BYTES=4294967296 - ETCD_SNAPSHOT_COUNT=50000 volumes: - ./volumes/etcd:/etcd command: etcd -advertise-client-urls=http://127.0.0.1:2379 -listen-client-urls http://0.0.0.0:2379 --data-dir /etcd minio: container_name: milvus-minio image: quay.io/minio/minio:RELEASE.2023-03-20T20-16-18Z environment: - MINIO_ROOT_USER=minioadmin - MINIO_ROOT_PASSWORD=minioadmin volumes: - ./volumes/minio:/minio_data command: minio server /minio_data standalone: container_name: milvus-standalone image: milvusdb/milvus:v2.5.0 # <-- 修正点在这里 command: ["milvus", "run", "standalone"] environment: - ETCD_ENDPOINTS=etcd:2379 - MINIO_ADDRESS=minio:9000 volumes: - ./volumes/milvus:/var/lib/milvus ports: - "19530:19530" # Milvus port - "9091:9091" # Milvus metrics port depends_on: - "etcd" - "minio" volumes: etcd: minio: milvus:这是我的docker-compose.yml,请帮我解决我的报错,或者更改我的代码让我避开这个问题

这个程序里的Q_ARG一直是报错的情况该怎么解决 # -*- coding: utf-8 -*- import sys import os import cv2 import numpy as np import math import time import logging import threading from collections import deque from PyQt5.QtWidgets import ( QApplication, QMainWindow, QPushButton, QWidget, QVBoxLayout, QHBoxLayout, QMessageBox, QLabel, QFileDialog, QToolBox, QComboBox, QStatusBar, QGroupBox, QSlider, QDockWidget, QProgressDialog, QLineEdit, QRadioButton, QGridLayout, QSpinBox, QCheckBox, QDialog, QDialogButtonBox, QDoubleSpinBox, QProgressBar ) from PyQt5.QtCore import QRect, Qt, QSettings, QThread, pyqtSignal, QTimer, QMetaObject, pyqtSlot from PyQt5.QtGui import QImage, QPixmap from CamOperation_class import CameraOperation from MvCameraControl_class import * import ctypes from ctypes import cast, POINTER from datetime import datetime import skimage import platform from CameraParams_header import ( MV_GIGE_DEVICE, MV_USB_DEVICE, MV_GENTL_CAMERALINK_DEVICE, MV_GENTL_CXP_DEVICE, MV_GENTL_XOF_DEVICE ) # ===== 全局配置 ===== # 模板匹配参数 MATCH_THRESHOLD = 0.75 # 降低匹配置信度阈值以提高灵敏度 MIN_MATCH_COUNT = 10 # 最小匹配特征点数量 MIN_FRAME_INTERVAL = 0.1 # 最小检测间隔(秒) # ===== 全局变量 ===== current_sample_path = "" detection_history = [] isGrabbing = False isOpen = False obj_cam_operation = None frame_monitor_thread = None template_matcher_thread = None MV_OK = 0 MV_E_CALLORDER = -2147483647 # ==================== 优化后的质量检测算法 ==================== def enhanced_check_print_quality(sample_image_path, test_image, threshold=0.05): # 不再使用传感器数据调整阈值 adjusted_threshold = threshold try: sample_img_data = np.fromfile(sample_image_path, dtype=np.uint8) sample_image = cv2.imdecode(sample_img_data, cv2.IMREAD_GRAYSCALE) if sample_image is None: logging.error(f"无法解码样本图像: {sample_image_path}") return None, None, None except Exception as e: logging.exception(f"样本图像读取异常: {str(e)}") return None, None, None if len(test_image.shape) == 3: test_image_gray = cv2.cvtColor(test_image, cv2.COLOR_BGR2GRAY) else: test_image_gray = test_image.copy() sample_image = cv2.GaussianBlur(sample_image, (5, 5), 0) test_image_gray = cv2.GaussianBlur(test_image_gray, (5, 5), 0) try: # 使用更鲁棒的SIFT特征检测器 sift = cv2.SIFT_create() keypoints1, descriptors1 = sift.detectAndCompute(sample_image, None) keypoints2, descriptors2 = sift.detectAndCompute(test_image_gray, None) if descriptors1 is None or descriptors2 is None: logging.warning("无法提取特征描述符,跳过配准") aligned_sample = sample_image else: # 使用FLANN匹配器提高匹配精度 FLANN_INDEX_KDTREE = 1 index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5) search_params = dict(checks=50) flann = cv2.FlannBasedMatcher(index_params, search_params) matches = flann.knnMatch(descriptors1, descriptors2, k=2) # 应用Lowe's比率测试筛选优质匹配 good_matches = [] for m, n in matches: if m.distance < 0.7 * n.distance: good_matches.append(m) if len(good_matches) > MIN_MATCH_COUNT: src_pts = np.float32([keypoints1[m.queryIdx].pt for m in good_matches]).reshape(-1, 1, 2) dst_pts = np.float32([keypoints2[m.trainIdx].pt for m in good_matches]).reshape(-1, 1, 2) H, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0) if H is not None: aligned_sample = cv2.warpPerspective( sample_image, H, (test_image_gray.shape[1], test_image_gray.shape[0]) ) logging.info("图像配准成功,使用配准后样本") else: aligned_sample = sample_image logging.warning("无法计算单应性矩阵,使用原始样本") else: aligned_sample = sample_image logging.warning(f"特征点匹配不足({len(good_matches)}/{MIN_MATCH_COUNT}),跳过图像配准") except Exception as e: logging.error(f"图像配准失败: {str(e)}") aligned_sample = sample_image try: if aligned_sample.shape != test_image_gray.shape: test_image_gray = cv2.resize(test_image_gray, (aligned_sample.shape[1], aligned_sample.shape[0])) except Exception as e: logging.error(f"图像调整大小失败: {str(e)}") return None, None, None try: from skimage.metrics import structural_similarity as compare_ssim ssim_score, ssim_diff = compare_ssim( aligned_sample, test_image_gray, full=True, gaussian_weights=True, data_range=255 ) except ImportError: from skimage.measure import compare_ssim ssim_score, ssim_diff = compare_ssim( aligned_sample, test_image_gray, full=True, gaussian_weights=True ) except Exception as e: logging.error(f"SSIM计算失败: {str(e)}") abs_diff = cv2.absdiff(aligned_sample, test_image_gray) ssim_diff = abs_diff.astype(np.float32) / 255.0 ssim_score = 1.0 - np.mean(ssim_diff) ssim_diff = (1 - ssim_diff) * 255 abs_diff = cv2.absdiff(aligned_sample, test_image_gray) combined_diff = cv2.addWeighted(ssim_diff.astype(np.uint8), 0.7, abs_diff, 0.3, 0) _, thresholded = cv2.threshold(combined_diff, 30, 255, cv2.THRESH_BINARY) kernel = np.ones((3, 3), np.uint8) thresholded = cv2.morphologyEx(thresholded, cv2.MORPH_OPEN, kernel) thresholded = cv2.morphologyEx(thresholded, cv2.MORPH_CLOSE, kernel) diff_pixels = np.count_nonzero(thresholded) total_pixels = aligned_sample.size diff_ratio = diff_pixels / total_pixels is_qualified = diff_ratio <= adjusted_threshold marked_image = cv2.cvtColor(test_image_gray, cv2.COLOR_GRAY2BGR) marked_image[thresholded == 255] = [0, 0, 255] # 放大缺陷标记 scale_factor = 2.0 # 放大2倍 marked_image = cv2.resize(marked_image, None, fx=scale_factor, fy=scale_factor, interpolation=cv2.INTER_LINEAR) labels = skimage.measure.label(thresholded) properties = skimage.measure.regionprops(labels) for prop in properties: if prop.area > 50: y, x = prop.centroid # 根据放大比例调整坐标 x_scaled = int(x * scale_factor) y_scaled = int(y * scale_factor) cv2.putText(marked_image, f"Defect", (x_scaled, y_scaled), cv2.FONT_HERSHEY_SIMPLEX, 0.5 * scale_factor, (0, 255, 255), int(scale_factor)) return is_qualified, diff_ratio, marked_image # ==================== 视觉触发的质量检测流程 ==================== def vision_controlled_check(capture_image=None, match_score=0.0): """修改为接受图像帧和匹配分数""" global current_sample_path, detection_history logging.info("视觉触发质量检测启动") # 如果没有提供图像,使用当前帧 if capture_image is None: frame = obj_cam_operation.get_current_frame() else: frame = capture_image if frame is None: QMessageBox.warning(mainWindow, "错误", "无法获取当前帧图像!", QMessageBox.Ok) return progress = QProgressDialog("正在检测...", "取消", 0, 100, mainWindow) progress.setWindowModality(Qt.WindowModal) progress.setValue(10) try: diff_threshold = mainWindow.sliderDiffThreshold.value() / 100.0 logging.info(f"使用差异度阈值: {diff_threshold}") progress.setValue(30) is_qualified, diff_ratio, marked_image = enhanced_check_print_quality( current_sample_path, frame, threshold=diff_threshold ) progress.setValue(70) if is_qualified is None: QMessageBox.critical(mainWindow, "检测错误", "检测失败,请检查日志", QMessageBox.Ok) return logging.info(f"检测结果: 合格={is_qualified}, 差异={diff_ratio}") progress.setValue(90) update_diff_display(diff_ratio, is_qualified) result_text = f"印花是否合格: {'合格' if is_qualified else '不合格'}\n差异占比: {diff_ratio*100:.2f}%\n阈值: {diff_threshold*100:.2f}%" QMessageBox.information(mainWindow, "检测结果", result_text, QMessageBox.Ok) if marked_image is not None: # 创建可调整大小的窗口 cv2.namedWindow("缺陷标记结果", cv2.WINDOW_NORMAL) cv2.resizeWindow("缺陷标记结果", 800, 600) # 初始大小 cv2.imshow("缺陷标记结果", marked_image) cv2.waitKey(0) cv2.destroyAllWindows() detection_result = { 'timestamp': datetime.now(), 'qualified': is_qualified, 'diff_ratio': diff_ratio, 'threshold': diff_threshold, 'trigger_type': 'vision' if capture_image else 'manual' } detection_history.append(detection_result) update_history_display() progress.setValue(100) except Exception as e: logging.exception("印花检测失败") QMessageBox.critical(mainWindow, "检测错误", f"检测过程中发生错误: {str(e)}", QMessageBox.Ok) finally: progress.close() # ==================== 相机操作函数 ==================== def open_device(): global deviceList, nSelCamIndex, obj_cam_operation, isOpen, frame_monitor_thread, mainWindow if isOpen: QMessageBox.warning(mainWindow, "Error", '相机已打开!', QMessageBox.Ok) return MV_E_CALLORDER nSelCamIndex = mainWindow.ComboDevices.currentIndex() if nSelCamIndex < 0: QMessageBox.warning(mainWindow, "Error", '请选择相机!', QMessageBox.Ok) return MV_E_CALLORDER # 创建相机控制对象 cam = MvCamera() # 初始化相机操作对象 obj_cam_operation = CameraOperation(cam, deviceList, nSelCamIndex) ret = obj_cam_operation.open_device() if 0 != ret: strError = "打开设备失败 ret:" + ToHexStr(ret) QMessageBox.warning(mainWindow, "Error", strError, QMessageBox.Ok) isOpen = False else: set_continue_mode() get_param() isOpen = True enable_controls() # 创建并启动帧监控线程 frame_monitor_thread = FrameMonitorThread(obj_cam_operation) frame_monitor_thread.frame_status.connect(mainWindow.statusBar().showMessage) frame_monitor_thread.start() def start_grabbing(): global obj_cam_operation, isGrabbing, template_matcher_thread ret = obj_cam_operation.start_grabbing(mainWindow.widgetDisplay.winId()) if ret != 0: strError = "开始取流失败 ret:" + ToHexStr(ret) QMessageBox.warning(mainWindow, "Error", strError, QMessageBox.Ok) else: isGrabbing = True enable_controls() # 等待第一帧到达 QThread.msleep(500) if not obj_cam_operation.is_frame_available(): QMessageBox.warning(mainWindow, "警告", "开始取流后未接收到帧,请检查相机连接!", QMessageBox.Ok) # 如果启用了自动检测,启动检测线程 if mainWindow.chkContinuousMatch.isChecked(): toggle_template_matching(True) def stop_grabbing(): global obj_cam_operation, isGrabbing, template_matcher_thread ret = obj_cam_operation.Stop_grabbing() if ret != 0: strError = "停止取流失败 ret:" + ToHexStr(ret) QMessageBox.warning(mainWindow, "Error", strError, QMessageBox.Ok) else: isGrabbing = False enable_controls() # 停止模板匹配线程 if template_matcher_thread and template_matcher_thread.isRunning(): template_matcher_thread.stop() def close_device(): global isOpen, isGrabbing, obj_cam_operation, frame_monitor_thread, template_matcher_thread if frame_monitor_thread and frame_monitor_thread.isRunning(): frame_monitor_thread.stop() frame_monitor_thread.wait(2000) # 停止模板匹配线程 if template_matcher_thread and template_matcher_thread.isRunning(): template_matcher_thread.stop() template_matcher_thread.wait(2000) template_matcher_thread = None if isOpen and obj_cam_operation: obj_cam_operation.close_device() isOpen = False isGrabbing = False enable_controls() # ==================== 连续帧匹配检测器 ==================== class ContinuousFrameMatcher(QThread): frame_processed = pyqtSignal(np.ndarray, float, bool) # 处理后的帧, 匹配分数, 是否匹配 match_score_updated = pyqtSignal(float) # 匹配分数更新信号 match_success = pyqtSignal(np.ndarray, float) # 匹配成功信号 (帧, 匹配分数) def __init__(self, cam_operation, parent=None): super().__init__(parent) self.cam_operation = cam_operation self.running = True self.sample_template = None self.min_match_count = MIN_MATCH_COUNT self.match_threshold = MATCH_THRESHOLD self.sample_kp = None self.sample_des = None self.current_match_score = 0.0 self.last_match_time = 0 self.frame_counter = 0 self.consecutive_fail_count = 0 self.last_trigger_time = 0 # 上次触发时间 self.cool_down = 0.2 # 冷却时间(秒) # 特征检测器 - 使用SIFT self.sift = cv2.SIFT_create() # 特征匹配器 - 使用FLANN提高匹配精度 FLANN_INDEX_KDTREE = 1 index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5) search_params = dict(checks=50) self.flann = cv2.FlannBasedMatcher(index_params, search_params) # 性能监控 self.processing_times = deque(maxlen=100) self.frame_rates = deque(maxlen=100) # 黑白相机优化 self.clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8)) def preprocess_image(self, image): """增强黑白图像特征提取""" # 如果是单通道图像,转换为三通道 if len(image.shape) == 2: image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR) # 对比度增强 (LAB空间) lab = cv2.cvtColor(image, cv2.COLOR_BGR2LAB) l, a, b = cv2.split(lab) cl = self.clahe.apply(l) limg = cv2.merge((cl, a, b)) enhanced = cv2.cvtColor(limg, cv2.COLOR_LAB2BGR) # 边缘增强 gray = cv2.cvtColor(enhanced, cv2.COLOR_BGR2GRAY) edges = cv2.Canny(gray, 50, 150) # 组合特征 return cv2.addWeighted(enhanced, 0.7, cv2.cvtColor(edges, cv2.COLOR_GRAY2BGR), 0.3, 0) def set_sample(self, sample_img): """设置标准样本并提取特征""" # 保存样本图像 self.sample_img = sample_img # 预处理增强特征 processed_sample = self.preprocess_image(sample_img) # 提取样本特征点 self.sample_kp, self.sample_des = self.sift.detectAndCompute(processed_sample, None) if self.sample_des is None or len(self.sample_kp) < self.min_match_count: logging.warning("样本特征点不足") return False logging.info(f"样本特征提取成功: {len(self.sample_kp)}个关键点") return True def process_frame(self, frame): """处理帧:特征提取、匹配和可视化""" is_matched = False match_score = 0.0 processed_frame = frame.copy() # 检查是否已设置样本 if self.sample_kp is None or self.sample_des is None: return processed_frame, match_score, is_matched # 预处理当前帧 processed_frame = self.preprocess_image(frame) # 转换为灰度图像用于特征提取 gray_frame = cv2.cvtColor(processed_frame, cv2.COLOR_BGR2GRAY) try: # 提取当前帧的特征点 kp, des = self.sift.detectAndCompute(gray_frame, None) if des is None or len(kp) < 10: # 特征点不足 return processed_frame, match_score, is_matched # 匹配特征点 matches = self.flann.knnMatch(self.sample_des, des, k=2) # 应用Lowe's比率测试 good_matches = [] for m, n in matches: if m.distance < 0.7 * n.distance: good_matches.append(m) # 计算匹配分数(匹配点数量占样本特征点数量的比例) if len(self.sample_kp) > 0: match_score = len(good_matches) / len(self.sample_kp) match_score = min(1.0, max(0.0, match_score)) # 确保在0-1范围内 else: match_score = 0.0 # 判断是否匹配成功 if len(good_matches) >= self.min_match_count and match_score >= self.match_threshold: is_matched = True # 在图像上绘制匹配结果 if len(gray_frame.shape) == 2: processed_frame = cv2.cvtColor(gray_frame, cv2.COLOR_GRAY2BGR) # 绘制匹配点 processed_frame = cv2.drawMatches( self.sample_img, self.sample_kp, processed_frame, kp, good_matches, None, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS ) # 在图像上显示匹配分数 cv2.putText(processed_frame, f"Match Score: {match_score:.2f}", (20, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2) # 更新当前匹配分数 self.current_match_score = match_score self.match_score_updated.emit(match_score) # 检查是否匹配成功且超过冷却时间 current_time = time.time() if is_matched and (current_time - self.last_trigger_time) > self.cool_down: self.last_trigger_time = current_time logging.info(f"匹配成功! 分数: {match_score:.2f}, 触发质量检测") # 发出匹配成功信号 (传递当前帧) self.match_success.emit(frame.copy(), match_score) except Exception as e: logging.error(f"帧处理错误: {str(e)}") return processed_frame, match_score, is_matched def set_threshold(self, threshold): """更新匹配阈值""" self.match_threshold = max(0.0, min(1.0, threshold)) logging.info(f"更新匹配阈值: {self.match_threshold:.2f}") def run(self): """主处理循环 - 连续处理每一帧""" logging.info("连续帧匹配线程启动") self.last_match_time = time.time() self.consecutive_fail_count = 0 while self.running: start_time = time.time() # 检查相机状态 if not self.cam_operation or not self.cam_operation.is_grabbing: if self.consecutive_fail_count % 10 == 0: logging.debug("相机未取流,等待...") time.sleep(0.1) self.consecutive_fail_count += 1 continue # 获取当前帧 frame = self.cam_operation.get_current_frame() if frame is None: self.consecutive_fail_count += 1 if self.consecutive_fail_count % 10 == 0: logging.warning(f"连续{self.consecutive_fail_count}次获取帧失败") time.sleep(0.05) continue self.consecutive_fail_count = 0 try: # 处理帧 processed_frame, match_score, is_matched = self.process_frame(frame) # 发送处理结果 self.frame_processed.emit(processed_frame, match_score, is_matched) except Exception as e: logging.error(f"帧处理错误: {str(e)}") # 控制处理频率 processing_time = time.time() - start_time sleep_time = max(0.01, MIN_FRAME_INTERVAL - processing_time) time.sleep(sleep_time) logging.info("连续帧匹配线程退出") # ==================== 模板匹配控制函数 ==================== def toggle_template_matching(state): global template_matcher_thread, current_sample_path logging.debug(f"切换连续匹配状态: {state}") if state == Qt.Checked and isGrabbing: # 确保已设置样本 if not current_sample_path: logging.warning("尝试启动连续匹配但未设置样本") QMessageBox.warning(mainWindow, "错误", "请先设置标准样本", QMessageBox.Ok) mainWindow.chkContinuousMatch.setChecked(False) return if template_matcher_thread is None: logging.info("创建新的连续帧匹配线程") template_matcher_thread = ContinuousFrameMatcher(obj_cam_operation) template_matcher_thread.frame_processed.connect(update_frame_display) template_matcher_thread.match_score_updated.connect(update_match_score_display) # 正确连接匹配成功信号到质量检测函数 template_matcher_thread.match_success.connect( lambda frame, score: vision_controlled_check(frame, score) ) # 加载样本图像 sample_img = cv2.imread(current_sample_path) if sample_img is None: logging.error("无法加载标准样本图像") QMessageBox.warning(mainWindow, "错误", "无法加载标准样本图像", QMessageBox.Ok) mainWindow.chkContinuousMatch.setChecked(False) return if not template_matcher_thread.set_sample(sample_img): logging.warning("标准样本特征不足") QMessageBox.warning(mainWindow, "错误", "标准样本特征不足", QMessageBox.Ok) mainWindow.chkContinuousMatch.setChecked(False) return if not template_matcher_thread.isRunning(): logging.info("启动连续帧匹配线程") template_matcher_thread.start() elif template_matcher_thread and template_matcher_thread.isRunning(): logging.info("停止连续帧匹配线程") template_matcher_thread.stop() # 重置匹配分数显示 update_match_score_display(0.0) # 重置帧显示 if obj_cam_operation and obj_cam_operation.is_frame_available(): frame = obj_cam_operation.get_current_frame() if frame is not None: display_frame = frame.copy() # 添加状态信息 cv2.putText(display_frame, "Continuous Matching Disabled", (20, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2) update_frame_display(display_frame, 0.0, False) def update_frame_display(frame, match_score, is_matched): """更新主显示窗口(线程安全)""" # 确保在GUI线程中执行 if QThread.currentThread() != QApplication.instance().thread(): QMetaObject.invokeMethod( mainWindow, "updateDisplay", Qt.QueuedConnection, Q_ARG(np.ndarray, frame), Q_ARG(float, match_score), Q_ARG(bool, is_matched) ) return # 如果已经在主线程,直接调用主窗口的更新方法 mainWindow.updateDisplay(frame, match_score, is_matched) def update_match_score_display(score): """更新匹配分数显示""" # 将分数转换为百分比显示 score_percent = score * 100 mainWindow.lblMatchScoreValue.setText(f"{score_percent:.1f}%") # 根据分数设置颜色 if score > 0.8: # 高于80%显示绿色 color = "green" elif score > 0.6: # 60%-80%显示黄色 color = "orange" else: # 低于60%显示红色 color = "red" mainWindow.lblMatchScoreValue.setStyleSheet(f"color: {color}; font-weight: bold;") def update_diff_display(diff_ratio, is_qualified): mainWindow.lblCurrentDiff.setText(f"当前差异度: {diff_ratio*100:.2f}%") if is_qualified: mainWindow.lblDiffStatus.setText("状态: 合格") mainWindow.lblDiffStatus.setStyleSheet("color: green; font-size: 12px;") else: mainWindow.lblDiffStatus.setText("状态: 不合格") mainWindow.lblDiffStatus.setStyleSheet("color: red; font-size: 12px;") def update_diff_threshold(value): mainWindow.lblDiffValue.setText(f"{value}%") def update_sample_display(): global current_sample_path if current_sample_path: mainWindow.lblSamplePath.setText(f"当前样本: {os.path.basename(current_sample_path)}") mainWindow.lblSamplePath.setToolTip(current_sample_path) mainWindow.bnPreviewSample.setEnabled(True) else: mainWindow.lblSamplePath.setText("当前样本: 未设置样本") mainWindow.bnPreviewSample.setEnabled(False) def update_history_display(): global detection_history mainWindow.cbHistory.clear() for i, result in enumerate(detection_history[-10:]): timestamp = result['timestamp'].strftime("%H:%M:%S") status = "合格" if result['qualified'] else "不合格" ratio = f"{result['diff_ratio']*100:.2f}%" trigger = "视觉" if result['trigger_type'] == 'vision' else "手动" mainWindow.cbHistory.addItem(f"[{trigger} {timestamp}] {status} - 差异: {ratio}") def update_match_threshold(value): """更新匹配阈值显示并应用到匹配器""" global template_matcher_thread # 更新UI显示 if mainWindow: mainWindow.lblThresholdValue.setText(f"{value}%") # 如果匹配线程存在,更新其匹配阈值 if template_matcher_thread: # 转换为0-1范围的浮点数 threshold = value / 100.0 template_matcher_thread.set_threshold(threshold) logging.debug(f"更新匹配阈值: {threshold:.2f}") # ==================== 主窗口类 ==================== class MainWindow(QMainWindow): def __init__(self): super().__init__() self.setWindowTitle("布料印花检测系统 - 连续匹配版") self.resize(1200, 800) central_widget = QWidget() self.setCentralWidget(central_widget) main_layout = QVBoxLayout(central_widget) # 设备枚举区域 device_layout = QHBoxLayout() self.ComboDevices = QComboBox() self.bnEnum = QPushButton("枚举设备") self.bnOpen = QPushButton("打开设备") self.bnClose = QPushButton("关闭设备") device_layout.addWidget(self.ComboDevices) device_layout.addWidget(self.bnEnum) device_layout.addWidget(self.bnOpen) device_layout.addWidget(self.bnClose) main_layout.addLayout(device_layout) # 取流控制组 self.groupGrab = QGroupBox("取流控制") grab_layout = QHBoxLayout(self.groupGrab) self.bnStart = QPushButton("开始取流") self.bnStop = QPushButton("停止取流") self.radioContinueMode = QRadioButton("连续模式") self.radioTriggerMode = QRadioButton("触发模式") self.bnSoftwareTrigger = QPushButton("软触发") grab_layout.addWidget(self.bnStart) grab_layout.addWidget(self.bnStop) grab_layout.addWidget(self.radioContinueMode) grab_layout.addWidget(self.radioTriggerMode) grab_layout.addWidget(self.bnSoftwareTrigger) main_layout.addWidget(self.groupGrab) # 参数设置组 self.paramgroup = QGroupBox("相机参数") param_layout = QGridLayout(self.paramgroup) self.edtExposureTime = QLineEdit() self.edtGain = QLineEdit() self.edtFrameRate = QLineEdit() self.bnGetParam = QPushButton("获取参数") self.bnSetParam = QPushButton("设置参数") self.bnSaveImage = QPushButton("保存图像") param_layout.addWidget(QLabel("曝光时间:"), 0, 0) param_layout.addWidget(self.edtExposureTime, 0, 1) param_layout.addWidget(self.bnGetParam, 0, 2) param_layout.addWidget(QLabel("增益:"), 1, 0) param_layout.addWidget(self.edtGain, 1, 1) param_layout.addWidget(self.bnSetParam, 1, 2) param_layout.addWidget(QLabel("帧率:"), 2, 0) param_layout.addWidget(self.edtFrameRate, 2, 1) param_layout.addWidget(self.bnSaveImage, 2, 2) main_layout.addWidget(self.paramgroup) # 图像显示区域 self.widgetDisplay = QLabel() self.widgetDisplay.setMinimumSize(640, 480) self.widgetDisplay.setStyleSheet("background-color: black;") self.widgetDisplay.setAlignment(Qt.AlignCenter) self.widgetDisplay.setText("相机预览区域") main_layout.addWidget(self.widgetDisplay, 1) # 创建自定义UI组件 self.setup_custom_ui() # 添加阈值自适应定时器 self.threshold_timer = QTimer() self.threshold_timer.timeout.connect(self.auto_adjust_threshold) self.threshold_timer.start(2000) # 每2秒调整一次 def auto_adjust_threshold(self): """根据环境亮度自动调整匹配阈值""" if not obj_cam_operation or not isGrabbing: return # 获取当前帧并计算平均亮度 frame = obj_cam_operation.get_current_frame() if frame is None: return gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) brightness = np.mean(gray) # 根据亮度动态调整阈值 (亮度低时降低阈值要求) if brightness < 50: # 暗环境 new_threshold = 40 # 40% elif brightness > 200: # 亮环境 new_threshold = 65 # 65% else: # 正常环境 new_threshold = 55 # 55% # 更新UI self.sliderThreshold.setValue(new_threshold) self.lblThresholdValue.setText(f"{new_threshold}%") # 更新匹配器阈值 update_match_threshold(new_threshold) # 状态栏显示调整信息 self.statusBar().showMessage(f"亮度: {brightness:.1f}, 自动调整阈值至: {new_threshold}%", 3000) def setup_custom_ui(self): # 工具栏 toolbar = self.addToolBar("检测工具") self.bnCheckPrint = QPushButton("手动检测") self.bnSaveSample = QPushButton("保存标准样本") self.bnPreviewSample = QPushButton("预览样本") self.cbHistory = QComboBox() self.cbHistory.setMinimumWidth(300) toolbar.addWidget(self.bnCheckPrint) toolbar.addWidget(self.bnSaveSample) toolbar.addWidget(self.bnPreviewSample) toolbar.addWidget(QLabel("历史记录:")) toolbar.addWidget(self.cbHistory) # 状态栏样本路径 self.lblSamplePath = QLabel("当前样本: 未设置样本") self.statusBar().addPermanentWidget(self.lblSamplePath) # 右侧面板 right_panel = QWidget() right_layout = QVBoxLayout(right_panel) right_layout.setContentsMargins(10, 10, 10, 10) # 差异度调整组 diff_group = QGroupBox("差异度调整") diff_layout = QVBoxLayout(diff_group) self.lblDiffThreshold = QLabel("差异度阈值 (0-100%):") self.sliderDiffThreshold = QSlider(Qt.Horizontal) self.sliderDiffThreshold.setRange(0, 100) self.sliderDiffThreshold.setValue(5) self.lblDiffValue = QLabel("5%") self.lblCurrentDiff = QLabel("当前差异度: -") self.lblCurrentDiff.setStyleSheet("font-size: 14px; font-weight: bold;") self.lblDiffStatus = QLabel("状态: 未检测") self.lblDiffStatus.setStyleSheet("font-size: 12px;") diff_layout.addWidget(self.lblDiffThreshold) diff_layout.addWidget(self.sliderDiffThreshold) diff_layout.addWidget(self.lblDiffValue) diff_layout.addWidget(self.lblCurrentDiff) diff_layout.addWidget(self.lblDiffStatus) right_layout.addWidget(diff_group) # ===== 连续匹配面板 ===== match_group = QGroupBox("连续帧匹配") match_layout = QVBoxLayout(match_group) # 样本设置 sample_layout = QHBoxLayout() self.bnSetSample = QPushButton("设置标准样本") self.bnPreviewSample = QPushButton("预览样本") self.lblSampleStatus = QLabel("状态: 未设置样本") sample_layout.addWidget(self.bnSetSample) sample_layout.addWidget(self.bnPreviewSample) sample_layout.addWidget(self.lblSampleStatus) match_layout.addLayout(sample_layout) # 匹配参数 param_layout = QHBoxLayout() self.lblMatchThreshold = QLabel("匹配阈值:") self.sliderThreshold = QSlider(Qt.Horizontal) self.sliderThreshold.setRange(50, 100) self.sliderThreshold.setValue(75) # 降低默认阈值 self.lblThresholdValue = QLabel("75%") param_layout.addWidget(self.lblMatchThreshold) param_layout.addWidget(self.sliderThreshold) param_layout.addWidget(self.lblThresholdValue) match_layout.addLayout(param_layout) # 匹配分数显示 match_score_layout = QHBoxLayout() self.lblMatchScore = QLabel("实时匹配分数:") self.lblMatchScoreValue = QLabel("0.0%") self.lblMatchScoreValue.setStyleSheet("font-weight: bold;") match_score_layout.addWidget(self.lblMatchScore) match_score_layout.addWidget(self.lblMatchScoreValue) match_layout.addLayout(match_score_layout) # 连续匹配开关 self.chkContinuousMatch = QCheckBox("启用连续帧匹配") self.chkContinuousMatch.setChecked(False) match_layout.addWidget(self.chkContinuousMatch) right_layout.addWidget(match_group) right_layout.addStretch(1) # 停靠窗口 dock = QDockWidget("检测控制面板", self) dock.setWidget(right_panel) dock.setFeatures(QDockWidget.DockWidgetMovable | QDockWidget.DockWidgetFloatable) self.addDockWidget(Qt.RightDockWidgetArea, dock) @pyqtSlot(np.ndarray, float, bool) def updateDisplay(self, frame, match_score, is_matched): """线程安全的显示更新方法(添加可视化仪表盘)""" # 创建可视化覆盖层 overlay = frame.copy() height, width = frame.shape[:2] # 绘制匹配度仪表盘 center_x, center_y = width - 100, 100 radius = 80 cv2.circle(overlay, (center_x, center_y), radius, (100, 100, 100), 3) # 绘制指针 (根据匹配度旋转) angle = 240 * match_score # 0-100% 对应 240度范围 end_x = center_x + int(radius * 0.8 * math.cos(math.radians(angle - 90))) end_y = center_y + int(radius * 0.8 * math.sin(math.radians(angle - 90))) cv2.line(overlay, (center_x, center_y), (end_x, end_y), (0, 200, 0), 3) # 添加文本标签 cv2.putText(overlay, f"匹配度: {match_score*100:.1f}%", (center_x - 70, center_y + radius + 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0) if match_score > 0.8 else (0, 0, 255) if match_score < 0.6 else (0, 165, 255), 2) # 添加状态标签 status_text = "匹配成功!" if is_matched else "检测中..." cv2.putText(overlay, status_text, (20, 40), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 255, 0) if is_matched else (200, 200, 0), 2) # 将OpenCV图像转换为Qt图像 if len(overlay.shape) == 3: # 彩色图像 h, w, ch = overlay.shape bytes_per_line = ch * w q_img = QImage(overlay.data, w, h, bytes_per_line, QImage.Format_RGB888) q_img = q_img.rgbSwapped() # BGR -> RGB else: # 灰度图像 h, w = overlay.shape bytes_per_line = w q_img = QImage(overlay.data, w, h, bytes_per_line, QImage.Format_Grayscale8) # 创建QPixmap并缩放 pixmap = QPixmap.fromImage(q_img) scaled_pixmap = pixmap.scaled( self.widgetDisplay.size(), Qt.KeepAspectRatio, Qt.SmoothTransformation ) # 更新显示 self.widgetDisplay.setPixmap(scaled_pixmap) self.widgetDisplay.setAlignment(Qt.AlignCenter) def closeEvent(self, event): logging.info("主窗口关闭,执行清理...") close_device() event.accept() # 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