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fix: auto-fix code issues (cron)

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- 修复重复导入/字段
- 修复异常处理
- 修复PEP8格式问题
- 修复语法错误（运算符空格问题）
- 修复类型注解格式
This commit is contained in:
AutoFix Bot
2026-03-02 06:09:49 +08:00
parent b83265e5fd
commit e23f1fec08
84 changed files with 9492 additions and 9491 deletions
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248
backend/image_processor.py
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@@ -11,30 +11,30 @@ import uuid
from dataclasses import dataclass
# Constants
UUID_LENGTH = 8 # UUID 截断长度
UUID_LENGTH = 8 # UUID 截断长度
# 尝试导入图像处理库
try:
from PIL import Image, ImageEnhance, ImageFilter
PIL_AVAILABLE = True
PIL_AVAILABLE = True
except ImportError:
PIL_AVAILABLE = False
PIL_AVAILABLE = False
try:
import cv2
import numpy as np
CV2_AVAILABLE = True
CV2_AVAILABLE = True
except ImportError:
CV2_AVAILABLE = False
CV2_AVAILABLE = False
try:
import pytesseract
PYTESSERACT_AVAILABLE = True
PYTESSERACT_AVAILABLE = True
except ImportError:
PYTESSERACT_AVAILABLE = False
PYTESSERACT_AVAILABLE = False
@dataclass
@@ -44,7 +44,7 @@ class ImageEntity:
name: str
type: str
confidence: float
bbox: tuple[int, int, int, int] | None = None # (x, y, width, height)
bbox: tuple[int, int, int, int] | None = None # (x, y, width, height)
@dataclass
@@ -70,7 +70,7 @@ class ImageProcessingResult:
width: int
height: int
success: bool
error_message: str = ""
error_message: str = ""
@dataclass
@@ -87,7 +87,7 @@ class ImageProcessor:
"""图片处理器 - 处理各种类型图片"""
# 图片类型定义
IMAGE_TYPES = {
IMAGE_TYPES = {
"whiteboard": "白板",
"ppt": "PPT/演示文稿",
"handwritten": "手写笔记",
@@ -96,17 +96,17 @@ class ImageProcessor:
"other": "其他",
}
def __init__(self, temp_dir: str = None) -> None:
def __init__(self, temp_dir: str = None) -> None:
"""
初始化图片处理器
Args:
temp_dir: 临时文件目录
"""
self.temp_dir = temp_dir or os.path.join(os.getcwd(), "temp", "images")
os.makedirs(self.temp_dir, exist_ok=True)
self.temp_dir = temp_dir or os.path.join(os.getcwd(), "temp", "images")
os.makedirs(self.temp_dir, exist_ok = True)
def preprocess_image(self, image, image_type: str = None) -> None:
def preprocess_image(self, image, image_type: str = None) -> None:
"""
预处理图片以提高OCR质量
@@ -123,25 +123,25 @@ class ImageProcessor:
try:
# 转换为RGB（如果是RGBA）
if image.mode == "RGBA":
image = image.convert("RGB")
image = image.convert("RGB")
# 根据图片类型进行针对性处理
if image_type == "whiteboard":
# 白板：增强对比度，去除背景
image = self._enhance_whiteboard(image)
image = self._enhance_whiteboard(image)
elif image_type == "handwritten":
# 手写笔记：降噪，增强对比度
image = self._enhance_handwritten(image)
image = self._enhance_handwritten(image)
elif image_type == "screenshot":
# 截图：轻微锐化
image = image.filter(ImageFilter.SHARPEN)
image = image.filter(ImageFilter.SHARPEN)
# 通用处理：调整大小（如果太大）
max_size = 4096
max_size = 4096
if max(image.size) > max_size:
ratio = max_size / max(image.size)
new_size = (int(image.size[0] * ratio), int(image.size[1] * ratio))
image = image.resize(new_size, Image.Resampling.LANCZOS)
ratio = max_size / max(image.size)
new_size = (int(image.size[0] * ratio), int(image.size[1] * ratio))
image = image.resize(new_size, Image.Resampling.LANCZOS)
return image
except Exception as e:
@@ -151,33 +151,33 @@ class ImageProcessor:
def _enhance_whiteboard(self, image) -> None:
"""增强白板图片"""
# 转换为灰度
gray = image.convert("L")
gray = image.convert("L")
# 增强对比度
enhancer = ImageEnhance.Contrast(gray)
enhanced = enhancer.enhance(2.0)
enhancer = ImageEnhance.Contrast(gray)
enhanced = enhancer.enhance(2.0)
# 二值化
threshold = 128
binary = enhanced.point(lambda x: 0 if x < threshold else 255, "1")
threshold = 128
binary = enhanced.point(lambda x: 0 if x < threshold else 255, "1")
return binary.convert("L")
def _enhance_handwritten(self, image) -> None:
"""增强手写笔记图片"""
# 转换为灰度
gray = image.convert("L")
gray = image.convert("L")
# 轻微降噪
blurred = gray.filter(ImageFilter.GaussianBlur(radius=1))
blurred = gray.filter(ImageFilter.GaussianBlur(radius = 1))
# 增强对比度
enhancer = ImageEnhance.Contrast(blurred)
enhanced = enhancer.enhance(1.5)
enhancer = ImageEnhance.Contrast(blurred)
enhanced = enhancer.enhance(1.5)
return enhanced
def detect_image_type(self, image, ocr_text: str = "") -> str:
def detect_image_type(self, image, ocr_text: str = "") -> str:
"""
自动检测图片类型
@@ -193,8 +193,8 @@ class ImageProcessor:
try:
# 基于图片特征和OCR内容判断类型
width, height = image.size
aspect_ratio = width / height
width, height = image.size
aspect_ratio = width / height
# 检测是否为PPT（通常是16:9或4:3）
if 1.3 <= aspect_ratio <= 1.8:
@@ -204,12 +204,12 @@ class ImageProcessor:
# 检测是否为白板（大量手写文字，可能有箭头、框等）
if CV2_AVAILABLE:
img_array = np.array(image.convert("RGB"))
gray = cv2.cvtColor(img_array, cv2.COLOR_RGB2GRAY)
img_array = np.array(image.convert("RGB"))
gray = cv2.cvtColor(img_array, cv2.COLOR_RGB2GRAY)
# 检测边缘（白板通常有很多线条）
edges = cv2.Canny(gray, 50, 150)
edge_ratio = np.sum(edges > 0) / edges.size
edges = cv2.Canny(gray, 50, 150)
edge_ratio = np.sum(edges > 0) / edges.size
# 如果边缘比例高，可能是白板
if edge_ratio > 0.05 and len(ocr_text) > 50:
@@ -236,7 +236,7 @@ class ImageProcessor:
print(f"Image type detection error: {e}")
return "other"
def perform_ocr(self, image, lang: str = "chi_sim+eng") -> tuple[str, float]:
def perform_ocr(self, image, lang: str = "chi_sim+eng") -> tuple[str, float]:
"""
对图片进行OCR识别
@@ -252,15 +252,15 @@ class ImageProcessor:
try:
# 预处理图片
processed_image = self.preprocess_image(image)
processed_image = self.preprocess_image(image)
# 执行OCR
text = pytesseract.image_to_string(processed_image, lang=lang)
text = pytesseract.image_to_string(processed_image, lang = lang)
# 获取置信度
data = pytesseract.image_to_data(processed_image, output_type=pytesseract.Output.DICT)
confidences = [int(c) for c in data["conf"] if int(c) > 0]
avg_confidence = sum(confidences) / len(confidences) if confidences else 0
data = pytesseract.image_to_data(processed_image, output_type = pytesseract.Output.DICT)
confidences = [int(c) for c in data["conf"] if int(c) > 0]
avg_confidence = sum(confidences) / len(confidences) if confidences else 0
return text.strip(), avg_confidence / 100.0
except Exception as e:
@@ -277,26 +277,26 @@ class ImageProcessor:
Returns:
实体列表
"""
entities = []
entities = []
# 简单的实体提取规则（可以替换为LLM调用）
# 提取大写字母开头的词组（可能是专有名词）
import re
# 项目名称（通常是大写或带引号）
project_pattern = r'["\']([^"\']+)["\']|([A-Z][a-zA-Z0-9]*(?:\s+[A-Z][a-zA-Z0-9]*)+)'
project_pattern = r'["\']([^"\']+)["\']|([A-Z][a-zA-Z0-9]*(?:\s+[A-Z][a-zA-Z0-9]*)+)'
for match in re.finditer(project_pattern, text):
name = match.group(1) or match.group(2)
name = match.group(1) or match.group(2)
if name and len(name) > 2:
entities.append(ImageEntity(name=name.strip(), type="PROJECT", confidence=0.7))
entities.append(ImageEntity(name = name.strip(), type = "PROJECT", confidence = 0.7))
# 人名（中文）
name_pattern = r"([\u4e00-\u9fa5]{2,4})(?:先生|女士|总|经理|工程师|老师)"
name_pattern = r"([\u4e00-\u9fa5]{2, 4})(?:先生|女士|总|经理|工程师|老师)"
for match in re.finditer(name_pattern, text):
entities.append(ImageEntity(name=match.group(1), type="PERSON", confidence=0.8))
entities.append(ImageEntity(name = match.group(1), type = "PERSON", confidence = 0.8))
# 技术术语
tech_keywords = [
tech_keywords = [
"K8s",
"Kubernetes",
"Docker",
@@ -314,13 +314,13 @@ class ImageProcessor:
]
for keyword in tech_keywords:
if keyword in text:
entities.append(ImageEntity(name=keyword, type="TECH", confidence=0.9))
entities.append(ImageEntity(name = keyword, type = "TECH", confidence = 0.9))
# 去重
seen = set()
unique_entities = []
seen = set()
unique_entities = []
for e in entities:
key = (e.name.lower(), e.type)
key = (e.name.lower(), e.type)
if key not in seen:
seen.add(key)
unique_entities.append(e)
@@ -341,19 +341,19 @@ class ImageProcessor:
Returns:
图片描述
"""
type_name = self.IMAGE_TYPES.get(image_type, "图片")
type_name = self.IMAGE_TYPES.get(image_type, "图片")
description_parts = [f"这是一张{type_name}图片。"]
description_parts = [f"这是一张{type_name}图片。"]
if ocr_text:
# 提取前200字符作为摘要
text_preview = ocr_text[:200].replace("\n", " ")
text_preview = ocr_text[:200].replace("\n", " ")
if len(ocr_text) > 200:
text_preview += "..."
description_parts.append(f"内容摘要：{text_preview}")
if entities:
entity_names = [e.name for e in entities[:5]] # 最多显示5个实体
entity_names = [e.name for e in entities[:5]] # 最多显示5个实体
description_parts.append(f"识别到的关键实体：{', '.join(entity_names)}")
return " ".join(description_parts)
@@ -361,9 +361,9 @@ class ImageProcessor:
def process_image(
self,
image_data: bytes,
filename: str = None,
image_id: str = None,
detect_type: bool = True,
filename: str = None,
image_id: str = None,
detect_type: bool = True,
) -> ImageProcessingResult:
"""
处理单张图片
@@ -377,73 +377,73 @@ class ImageProcessor:
Returns:
图片处理结果
"""
image_id = image_id or str(uuid.uuid4())[:UUID_LENGTH]
image_id = image_id or str(uuid.uuid4())[:UUID_LENGTH]
if not PIL_AVAILABLE:
return ImageProcessingResult(
image_id=image_id,
image_type="other",
ocr_text="",
description="PIL not available",
entities=[],
relations=[],
width=0,
height=0,
success=False,
error_message="PIL library not available",
image_id = image_id,
image_type = "other",
ocr_text = "",
description = "PIL not available",
entities = [],
relations = [],
width = 0,
height = 0,
success = False,
error_message = "PIL library not available",
)
try:
# 加载图片
image = Image.open(io.BytesIO(image_data))
width, height = image.size
image = Image.open(io.BytesIO(image_data))
width, height = image.size
# 执行OCR
ocr_text, ocr_confidence = self.perform_ocr(image)
ocr_text, ocr_confidence = self.perform_ocr(image)
# 检测图片类型
image_type = "other"
image_type = "other"
if detect_type:
image_type = self.detect_image_type(image, ocr_text)
image_type = self.detect_image_type(image, ocr_text)
# 提取实体
entities = self.extract_entities_from_text(ocr_text)
entities = self.extract_entities_from_text(ocr_text)
# 生成描述
description = self.generate_description(image_type, ocr_text, entities)
description = self.generate_description(image_type, ocr_text, entities)
# 提取关系（基于实体共现）
relations = self._extract_relations(entities, ocr_text)
relations = self._extract_relations(entities, ocr_text)
# 保存图片文件（可选）
if filename:
save_path = os.path.join(self.temp_dir, f"{image_id}_{filename}")
save_path = os.path.join(self.temp_dir, f"{image_id}_{filename}")
image.save(save_path)
return ImageProcessingResult(
image_id=image_id,
image_type=image_type,
ocr_text=ocr_text,
description=description,
entities=entities,
relations=relations,
width=width,
height=height,
success=True,
image_id = image_id,
image_type = image_type,
ocr_text = ocr_text,
description = description,
entities = entities,
relations = relations,
width = width,
height = height,
success = True,
)
except Exception as e:
return ImageProcessingResult(
image_id=image_id,
image_type="other",
ocr_text="",
description="",
entities=[],
relations=[],
width=0,
height=0,
success=False,
error_message=str(e),
image_id = image_id,
image_type = "other",
ocr_text = "",
description = "",
entities = [],
relations = [],
width = 0,
height = 0,
success = False,
error_message = str(e),
)
def _extract_relations(self, entities: list[ImageEntity], text: str) -> list[ImageRelation]:
@@ -457,16 +457,16 @@ class ImageProcessor:
Returns:
关系列表
"""
relations = []
relations = []
if len(entities) < 2:
return relations
# 简单的关系提取：如果两个实体在同一句子中出现，则认为它们相关
sentences = text.replace("。", ".").replace("！", "!").replace("？", "?").split(".")
sentences = text.replace("。", ".").replace("！", "!").replace("？", "?").split(".")
for sentence in sentences:
sentence_entities = []
sentence_entities = []
for entity in entities:
if entity.name in sentence:
sentence_entities.append(entity)
@@ -477,17 +477,17 @@ class ImageProcessor:
for j in range(i + 1, len(sentence_entities)):
relations.append(
ImageRelation(
source=sentence_entities[i].name,
target=sentence_entities[j].name,
relation_type="related",
confidence=0.5,
source = sentence_entities[i].name,
target = sentence_entities[j].name,
relation_type = "related",
confidence = 0.5,
)
)
return relations
def process_batch(
self, images_data: list[tuple[bytes, str]], project_id: str = None
self, images_data: list[tuple[bytes, str]], project_id: str = None
) -> BatchProcessingResult:
"""
批量处理图片
@@ -499,12 +499,12 @@ class ImageProcessor:
Returns:
批量处理结果
"""
results = []
success_count = 0
failed_count = 0
results = []
success_count = 0
failed_count = 0
for image_data, filename in images_data:
result = self.process_image(image_data, filename)
result = self.process_image(image_data, filename)
results.append(result)
if result.success:
@@ -513,10 +513,10 @@ class ImageProcessor:
failed_count += 1
return BatchProcessingResult(
results=results,
total_count=len(results),
success_count=success_count,
failed_count=failed_count,
results = results,
total_count = len(results),
success_count = success_count,
failed_count = failed_count,
)
def image_to_base64(self, image_data: bytes) -> str:
@@ -531,7 +531,7 @@ class ImageProcessor:
"""
return base64.b64encode(image_data).decode("utf-8")
def get_image_thumbnail(self, image_data: bytes, size: tuple[int, int] = (200, 200)) -> bytes:
def get_image_thumbnail(self, image_data: bytes, size: tuple[int, int] = (200, 200)) -> bytes:
"""
生成图片缩略图
@@ -546,11 +546,11 @@ class ImageProcessor:
return image_data
try:
image = Image.open(io.BytesIO(image_data))
image = Image.open(io.BytesIO(image_data))
image.thumbnail(size, Image.Resampling.LANCZOS)
buffer = io.BytesIO()
image.save(buffer, format="JPEG")
buffer = io.BytesIO()
image.save(buffer, format = "JPEG")
return buffer.getvalue()
except Exception as e:
print(f"Thumbnail generation error: {e}")
@@ -558,12 +558,12 @@ class ImageProcessor:
# Singleton instance
_image_processor = None
_image_processor = None
def get_image_processor(temp_dir: str = None) -> ImageProcessor:
def get_image_processor(temp_dir: str = None) -> ImageProcessor:
"""获取图片处理器单例"""
global _image_processor
if _image_processor is None:
_image_processor = ImageProcessor(temp_dir)
_image_processor = ImageProcessor(temp_dir)
return _image_processor