first commit

image_processor.py

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+import os
+import uuid
+from PIL import Image, ImageOps
+from typing import Tuple, Optional
+from config import settings
+
+class ImageProcessor:
+    def __init__(self):
+        self.width = settings.ink_width
+        self.height = settings.ink_height
+        self.upload_dir = settings.upload_dir
+        self.processed_dir = settings.processed_dir
+        
+        # 确保目录存在
+        os.makedirs(self.upload_dir, exist_ok=True)
+        os.makedirs(self.processed_dir, exist_ok=True)
+    
+    def process_image(self, image_path: str, grayscale: bool = True, dither: bool = True) -> str:
+        """
+        处理上传的图片，使其适配墨水屏显示
+        
+        Args:
+            image_path: 原始图片路径
+            grayscale: 是否转换为灰度图
+            dither: 是否使用抖动算法
+            
+        Returns:
+            处理后图片的相对路径
+        """
+        try:
+            # 打开原始图片
+            img = Image.open(image_path)
+            
+            # 转换为RGB模式（处理RGBA等格式）
+            if img.mode != 'RGB':
+                img = img.convert('RGB')
+            
+            # 自动旋转（基于EXIF信息）
+            img = ImageOps.exif_transpose(img)
+            
+            # 计算缩放比例，保持宽高比
+            img_ratio = img.width / img.height
+            target_ratio = self.width / self.height
+            
+            if img_ratio > target_ratio:
+                # 图片较宽，以高度为准
+                new_height = self.height
+                new_width = int(self.height * img_ratio)
+            else:
+                # 图片较高，以宽度为准
+                new_width = self.width
+                new_height = int(self.width / img_ratio)
+            
+            # 缩放图片
+            img = img.resize((new_width, new_height), Image.LANCZOS)
+            
+            # 居中裁剪到目标尺寸
+            left = (new_width - self.width) // 2
+            top = (new_height - self.height) // 2
+            right = left + self.width
+            bottom = top + self.height
+            img = img.crop((left, top, right, bottom))
+            
+            # 转换为灰度图
+            if grayscale:
+                img = img.convert('L')
+            
+            # 生成处理后的文件名
+            filename = f"{uuid.uuid4()}.bmp"
+            processed_path = os.path.join(self.processed_dir, filename)
+            
+            # 保存为BMP格式（墨水屏易解析）
+            if grayscale:
+                # 黑白图片，使用抖动算法
+                if dither:
+                    img.convert('1').save(processed_path)
+                else:
+                    img.convert('1', dither=Image.NONE).save(processed_path)
+            else:
+                # 彩色图片，转换为RGB模式
+                img.save(processed_path)
+            
+            # 返回相对路径
+            return os.path.relpath(processed_path)
+            
+        except Exception as e:
+            raise Exception(f"图片处理失败: {str(e)}")
+    
+    def save_upload(self, file_data: bytes, filename: str) -> str:
+        """
+        保存上传的原始文件
+        
+        Args:
+            file_data: 文件二进制数据
+            filename: 原始文件名
+            
+        Returns:
+            保存后的文件路径
+        """
+        # 生成唯一文件名
+        file_ext = os.path.splitext(filename)[1]
+        unique_filename = f"{uuid.uuid4()}{file_ext}"
+        upload_path = os.path.join(self.upload_dir, unique_filename)
+        
+        # 保存文件
+        with open(upload_path, "wb") as f:
+            f.write(file_data)
+        
+        return upload_path
+    
+    def get_image_info(self, image_path: str) -> dict:
+        """
+        获取图片信息
+        
+        Args:
+            image_path: 图片路径
+            
+        Returns:
+            图片信息字典
+        """
+        try:
+            with Image.open(image_path) as img:
+                return {
+                    "width": img.width,
+                    "height": img.height,
+                    "mode": img.mode,
+                    "format": img.format,
+                    "size_bytes": os.path.getsize(image_path)
+                }
+        except Exception as e:
+            raise Exception(f"获取图片信息失败: {str(e)}")
+
+# 全局图片处理器实例
+image_processor = ImageProcessor()