Python图片压缩处理

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~~~

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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~~