303 lines
12 KiB
Python
303 lines
12 KiB
Python
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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import os
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import logging
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from PIL import Image
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import numpy as np
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from typing import Tuple, Union, Optional
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import psutil
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import gc # 添加垃圾回收模块
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class ImageProcessor:
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"""
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图像处理工具类，提供智能图像加载和压缩功能
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"""
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def __init__(self, max_memory_percent=80, target_max_pixels=4000000):
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"""
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初始化图像处理器
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Args:
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max_memory_percent: 最大内存使用百分比，超过此值将更激进地压缩图像
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target_max_pixels: 目标最大像素数（宽×高），用于控制图像大小
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"""
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self.max_memory_percent = max_memory_percent
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self.target_max_pixels = target_max_pixels
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self.logger = logging.getLogger(self.__class__.__name__)
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# 记录已加载的图像，用于后续清理
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self.loaded_images = []
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def get_memory_usage(self) -> Tuple[float, float]:
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"""
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获取当前内存使用情况
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Returns:
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(已用内存百分比, 可用内存MB)
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"""
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memory = psutil.virtual_memory()
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return memory.percent, memory.available / (1024 * 1024)
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def check_memory_pressure(self) -> bool:
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"""
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检查内存压力，如果内存使用率超过阈值，执行垃圾回收
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Returns:
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是否存在内存压力
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"""
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mem_percent, available_mb = self.get_memory_usage()
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# 如果内存使用率超过阈值或可用内存低于100MB，认为存在内存压力
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if mem_percent > self.max_memory_percent or available_mb < 100:
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self.logger.warning(f"内存压力过大: 使用率 {mem_percent:.1f}%, 可用 {available_mb:.1f}MB，执行垃圾回收")
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self.force_garbage_collection()
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return True
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return False
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def force_garbage_collection(self):
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"""
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强制执行垃圾回收并清理已加载的图像资源
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"""
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# 释放已加载的图像
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self.release_loaded_images()
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# 执行多次垃圾回收
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for _ in range(3):
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gc.collect()
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# 记录垃圾回收后的内存状态
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mem_percent, available_mb = self.get_memory_usage()
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self.logger.info(f"垃圾回收后内存状态: 使用率 {mem_percent:.1f}%, 可用 {available_mb:.1f}MB")
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def release_loaded_images(self):
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"""
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释放所有已加载的图像资源
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"""
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for img in self.loaded_images:
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try:
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if hasattr(img, 'close') and callable(img.close):
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img.close()
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except Exception as e:
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self.logger.error(f"释放图像资源失败: {e}")
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# 清空列表
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self.loaded_images = []
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self.logger.info("已释放所有已加载的图像资源")
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def calculate_resize_factor(self, width: int, height: int) -> float:
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"""
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根据图像尺寸计算压缩因子
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Args:
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width: 图像宽度
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height: 图像高度
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Returns:
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压缩因子 (0.1-1.0)
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"""
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# 计算原始像素数
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original_pixels = width * height
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# 如果小于目标像素数，不需要压缩
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if original_pixels <= self.target_max_pixels:
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return 1.0
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# 计算基础压缩因子
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base_factor = (self.target_max_pixels / original_pixels) ** 0.5
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# 检查内存使用情况
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mem_percent, available_mb = self.get_memory_usage()
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# 根据内存使用情况调整压缩因子
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if mem_percent > self.max_memory_percent:
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# 内存紧张，增加压缩率
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memory_factor = 1.0 - ((mem_percent - self.max_memory_percent) / 20)
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memory_factor = max(0.5, memory_factor) # 至少保留50%质量
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base_factor *= memory_factor
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self.logger.warning(f"内存使用率高 ({mem_percent:.1f}%)，增加压缩率，压缩因子调整为 {base_factor:.2f}")
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# 确保压缩因子在合理范围内
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return max(0.1, min(1.0, base_factor))
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def smart_load_image(self, image_path: str, target_size: Optional[Tuple[int, int]] = None) -> Image.Image:
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"""
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智能加载图像，根据内存情况和图像大小自动调整尺寸
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Args:
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image_path: 图像路径
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target_size: 目标尺寸，如果指定则直接调整到此尺寸
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Returns:
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加载并调整大小后的PIL图像对象
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"""
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# 先检查内存压力
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self.check_memory_pressure()
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try:
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# 检查文件是否存在
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if not os.path.exists(image_path):
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self.logger.error(f"图像文件不存在: {image_path}")
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# 返回一个空白图像
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return Image.new('RGB', target_size or (800, 600), (240, 240, 240))
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# 获取文件大小（MB）
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file_size_mb = os.path.getsize(image_path) / (1024 * 1024)
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# 如果文件过大，先检查内存
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if file_size_mb > 10: # 大于10MB的文件
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mem_percent, available_mb = self.get_memory_usage()
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self.logger.info(f"大文件 ({file_size_mb:.1f}MB), 内存使用率: {mem_percent:.1f}%, 可用: {available_mb:.1f}MB")
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if mem_percent > 90 or available_mb < file_size_mb * 5:
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# 内存紧张，使用更保守的加载方式
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self.logger.warning(f"内存不足，使用保守加载方式")
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result = self._conservative_load(image_path, target_size)
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self.loaded_images.append(result)
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return result
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# 常规加载
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with Image.open(image_path) as img:
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# 获取原始尺寸
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original_width, original_height = img.size
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self.logger.debug(f"原始图像尺寸: {original_width}x{original_height}")
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# 如果指定了目标尺寸，直接调整
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if target_size:
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result = img.resize(target_size, Image.Resampling.LANCZOS)
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self.loaded_images.append(result)
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return result
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# 计算压缩因子
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resize_factor = self.calculate_resize_factor(original_width, original_height)
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if resize_factor < 1.0:
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# 需要压缩
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new_width = int(original_width * resize_factor)
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new_height = int(original_height * resize_factor)
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self.logger.info(f"压缩图像 {image_path} 从 {original_width}x{original_height} 到 {new_width}x{new_height}")
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result = img.resize((new_width, new_height), Image.Resampling.LANCZOS)
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self.loaded_images.append(result)
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return result
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else:
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# 不需要压缩，返回原图副本
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result = img.copy()
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self.loaded_images.append(result)
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return result
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except Exception as e:
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self.logger.error(f"加载图像 {image_path} 失败: {e}")
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# 返回一个空白图像
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return Image.new('RGB', target_size or (800, 600), (240, 240, 240))
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def _conservative_load(self, image_path: str, target_size: Optional[Tuple[int, int]] = None) -> Image.Image:
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"""
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保守加载大图像，先获取尺寸，然后以较低质量加载
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Args:
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image_path: 图像路径
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target_size: 目标尺寸
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Returns:
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加载的PIL图像对象
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"""
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try:
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# 先只获取图像信息，不加载像素数据
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with Image.open(image_path) as img:
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original_width, original_height = img.size
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format = img.format
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# 计算合适的缩小尺寸
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if target_size:
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new_width, new_height = target_size
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else:
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# 计算一个非常保守的压缩因子
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pixels = original_width * original_height
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conservative_factor = min(0.5, (2000000 / pixels) ** 0.5)
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new_width = int(original_width * conservative_factor)
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new_height = int(original_height * conservative_factor)
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# 使用缩略图方式加载
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with Image.open(image_path) as img:
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img.thumbnail((new_width, new_height), Image.Resampling.LANCZOS)
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# 创建新图像以确保释放原始文件句柄
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result = img.copy()
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self.logger.info(f"保守加载图像 {image_path} 从 {original_width}x{original_height} 到 {new_width}x{new_height}")
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return result
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except Exception as e:
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self.logger.error(f"保守加载图像失败: {e}")
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return Image.new('RGB', target_size or (800, 600), (240, 240, 240))
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def batch_process_directory(self, directory: str, max_images: int = 100) -> list:
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"""
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批量处理目录中的图像，返回处理后的图像列表
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Args:
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directory: 图像目录
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max_images: 最大处理图像数量
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Returns:
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处理后的PIL图像对象列表
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"""
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if not os.path.exists(directory):
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self.logger.error(f"目录不存在: {directory}")
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return []
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# 获取所有图像文件
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image_files = []
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for root, _, files in os.walk(directory):
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for file in files:
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if file.lower().endswith(('.png', '.jpg', '.jpeg', '.webp', '.bmp')):
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image_files.append(os.path.join(root, file))
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# 限制图像数量
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if len(image_files) > max_images:
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self.logger.warning(f"图像文件过多 ({len(image_files)}), 限制为 {max_images} 个")
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image_files = image_files[:max_images]
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# 批量加载图像
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images = []
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for image_file in image_files:
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img = self.smart_load_image(image_file)
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if img:
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images.append(img)
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# 每处理5张图片检查一次内存压力
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if len(images) % 5 == 0:
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self.check_memory_pressure()
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self.logger.info(f"已处理 {len(images)}/{len(image_files)} 个图像")
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return images
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def save_optimized_image(self, image: Image.Image, output_path: str, quality: int = 85) -> bool:
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"""
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保存优化后的图像
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Args:
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image: PIL图像对象
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output_path: 输出路径
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quality: JPEG质量 (1-100)
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Returns:
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是否保存成功
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"""
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try:
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# 确保输出目录存在
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os.makedirs(os.path.dirname(os.path.abspath(output_path)), exist_ok=True)
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# 检查图像模式，如果是RGBA且保存为JPEG，先转换为RGB
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if image.mode == 'RGBA' and (output_path.lower().endswith('.jpg') or output_path.lower().endswith('.jpeg')):
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image = image.convert('RGB')
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# 保存图像
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image.save(output_path, quality=quality, optimize=True)
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self.logger.info(f"图像已保存至: {output_path}")
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# 保存后检查内存压力
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self.check_memory_pressure()
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return True
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except Exception as e:
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self.logger.error(f"保存图像失败: {e}")
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return False
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