百木园前言
不知道有没有人跟我有一样的烦恼，有时候图片太大了占内存很烦，本来手机内存也就那么点，放一个图片稍微大一点的，都不
能放一个成百上千张，这不是很烦嘛。于是，这又让我来灵感了，既然图片给了我难题，那么我就来接受这样的挑战。所以，我
决定用python来试试可不可以压缩图片，不是不知道，一试就成功了，那么好的东西怎么能一个人独享呢，当然要分享出来给大
家呀~~~
python学习交流Q群：906715085### dynamic_quality.py import PIL.Image from math import log from SSIM_PIL import compare_ssim # pip install SSIM-PIL from PIL import ImageFile ImageFile.LOAD_TRUNCATED_IMAGES = True def get_ssim_at_quality(photo, quality): \"\"\" Return the ssim for this JPEG image saved at the specified quality \"\"\" ssim_photo = \"tmp.jpg\" # optimize is omitted here as it doesn\'t affect # quality but requires additional memory and cpu photo.save(ssim_photo, format=\"JPEG\", quality=quality, progressive=True) ssim_score = compare_ssim(photo, PIL.Image.open(ssim_photo)) return ssim_score def _ssim_iteration_count(lo, hi): \"\"\" Return the depth of the binary search tree for this range \"\"\" if lo >= hi: return 0 else: return int(log(hi - lo, 2)) + 1 def jpeg_dynamic_quality(original_photo): \"\"\" Return an integer representing the quality that this JPEG image should be saved at to attain the quality threshold specified for this photo class. Args: original_photo - a prepared PIL JPEG image (only JPEG is supported) \"\"\" ssim_goal = 0.9 #the original value is 0.95 hi = 35 #the original value is 85 lo = 30 #the original value is 80 # working on a smaller size image doesn\'t give worse results but is faster # changing this value requires updating the calculated thresholds photo = original_photo.resize((200, 200)) # if not _should_use_dynamic_quality(): # default_ssim = get_ssim_at_quality(photo, hi) # return hi, default_ssim # 95 is the highest useful value for JPEG. Higher values cause different behavior # Used to establish the image\'s intrinsic ssim without encoder artifacts normalized_ssim = get_ssim_at_quality(photo, 10) selected_quality = selected_ssim = None # loop bisection. ssim function increases monotonically so this will converge for i in range(_ssim_iteration_count(lo, hi)): curr_quality = (lo + hi) // 2 curr_ssim = get_ssim_at_quality(photo, curr_quality) ssim_ratio = curr_ssim / normalized_ssim if ssim_ratio >= ssim_goal: # continue to check whether a lower quality level also exceeds the goal selected_quality = curr_quality selected_ssim = curr_ssim hi = curr_quality else: lo = curr_quality if selected_quality: return selected_quality, selected_ssim else: default_ssim = get_ssim_at_quality(photo, hi) return hi, default_ssim
test.py
from PIL import Image from dynamic_quality import * def compress(filename,originpath,targetpath): name = filename.rstrip(\'.png\').rstrip(\'.jpg\') im = Image.open(originpath+filename) # print(im.format,im.size,im.mode) im = im.convert(\'RGB\') im.format = \"JPEG\" new_photo = im.copy() new_photo.thumbnail(im.size,resample=Image.ANTIALIAS) save_args = {\'format\':im.format} # print(save_args) # if im.format==\'JPEG\': # save_args[\'quality\']=20 save_args[\'quality\'],value=jpeg_dynamic_quality(im) save_args[\'optimize\']=True save_args[\'progressive=True\']=True # print(\"JPEG Quality Changed\") # elif im.format==\'PNG\': # save_args[\'format\']=\'JPEG\' # save_args[\'quality\']=5 # print(\"PNG Quality Changed\") new_photo.save(targetpath+name+\".jpg\",**save_args) if __name__ == \'__main__\': import os originpath = \"D:\\\\images\\\\img\\\\\" # 需要压缩图片路径 targetpath = \"D:\\\\images\\\\dangdang_image\\\\\" # 压缩完图片路径 for root, dirs, files in os.walk(originpath): for file in files: compress(file,originpath,targetpath)
最后
今天教大家的图片压缩到这里就结束了，喜欢的小伙伴记得点赞收藏。你不支持我，怎么能第一时间找到我，关于这篇文章有不
懂的地方可以评论留言哟！！我看到都会第一时间回复的，这一篇到这里就有翻过去了，下一章见啦~~
来源：https://www.cnblogs.com/1234567FENG/p/16386867.html
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