如何使用python对图片进行批量压缩详解

from PIL import Image
from io import StringIO
import dynamic_quality

im = Image.open("photo.jpg")
print(im.fORMat,im.size,im.mode)

new_photo = im.copy()
new_photo.thumbnail(im.size,resample=Image.ANTIALIAS)
save_args = {'format':im.format}
if im.format=='JPEG':
    save_args['quality'].value=85

new_photo.save("copy_photo.jpg",**save_args)

...
if im.format=='JPEG':
    save_args['quality'].value=85
    save_args['optimize']=True
...

...
if im.format=='JPEG':
    save_args['quality'].value=85
    save_args['optimize']=True
    save_args['progressive=True']=True
...

import PIL.Image
from math import log
from SSIM_PIL import compare_ssim


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 = 0.95
    hi = 85
    lo = 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((400, 400))

    # 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, 95)
    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

...
if im.format=='JPEG':
    save_args['quality'],value=dynamic_quality.jpeg_dynamic_quality(im)
    save_args['optimize']=True
    save_args['progressive']=True
...

from selenium import WEBdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support.wait import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.support.ui import Select
import os
import re
driver = webdriver.Chrome('C:/Users/admin/AppData/Local/Google/Chrome/Application/chromedriver.exe')
# 列出目录下所有的图片，存在 images 这个列表中
images = os.listdir('C:/Users/admin/Pictures/Saved Pictures')
# 处理所有图片
for i in range(len(images)):
    # 构建图片路径
    path = 'C:/Users/admin/Pictures/Saved Pictures/' + images[i]
    # 尝试处理所有图片
    try:
        # 打开 Squoosh
        driver.get('https://squoosh.app')
        # 找到输入框
        input_box = driver.find_element_by_xpath('.//input[@class="_2zg9i"]')
        # 输入图片路径
        input_box.send_keys(path)
        #设置图片格式
        select1 = Select(driver.find_elements_by_CSS_selector('select')[-1])
        if re.match('.*.png',images[i]):
            select1.select_by_value("png")
        if re.match('.*.jpg',images[i]):
            select1.select_by_value("mozjpeg")

        # 等待出现 'smaller'字样，10秒不出现则视为处理失败
        locator = (By.XPATH, './/span[@class="_1eNmr _1U8bE"][last()]')
        WebDriverWait(driver, 25).until(EC.text_to_be_present_in_element(locator, 'smaller'))

        # 找到下载按钮
        button = driver.find_elements_by_xpath('.//a[@title="Download"]')[-1]
        # 点击下载按钮
        button.click()
    # 输出处理失败的图片路径
    except:
        print('*'*30)
        print('Error: '+ path +' failed!')
        print('*'*30)
        continue