取消图片压缩功能
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ktianc
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@ -14,7 +14,6 @@ from oss2.models import PutObjectResult
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from core.exception import CustomException
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from core.logger import logger
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from utils import status
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from utils.file.compress.cpressJPG import compress_jpg_png
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from utils.file.file_manage import FileManage
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from utils.file.file_base import FileBase
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@ -47,13 +46,12 @@ class AliyunOSS(FileBase):
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self.bucket = oss2.Bucket(auth, bucket.endpoint, bucket.bucket)
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self.baseUrl = bucket.baseUrl
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async def upload_image(self, path: str, file: UploadFile, compress: bool = False, max_size: int = 10) -> str:
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async def upload_image(self, path: str, file: UploadFile, max_size: int = 10) -> str:
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"""
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上传图片
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:param path: path由包含文件后缀，不包含Bucket名称组成的Object完整路径，例如abc/efg/123.jpg。
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:param file: 文件对象
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:param compress: 是否压缩该文件
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:param max_size: 图片文件最大值，单位 MB，默认 10MB
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:return: 上传后的文件oss链接
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"""
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@ -61,14 +59,7 @@ class AliyunOSS(FileBase):
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await self.validate_file(file, max_size, self.IMAGE_ACCEPT)
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# 生成文件路径
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path = self.generate_static_file_path(path, file.filename)
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if compress:
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# 压缩图片
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file_path = await FileManage.async_save_temp_file(file)
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new_file = compress_jpg_png(file_path, originpath=os.path.abspath(file_path))
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with open(new_file, "rb") as f:
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file_data = f.read()
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else:
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file_data = await file.read()
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file_data = await file.read()
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return await self.__upload_file_to_oss(path, file_data)
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async def upload_video(self, path: str, file: UploadFile, max_size: int = 100) -> str:
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@ -1,52 +0,0 @@
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from PIL import Image, ExifTags # 安装依赖包：pip3 install pillow
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from utils.file.compress import dynamic_quality
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import os
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import time
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"""
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PIL读取的图像发生自动旋转：https://blog.csdn.net/mizhenpeng/article/details/82794112
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使用python批量压缩图片文件：https://blog.csdn.net/weixin_41855010/article/details/120723943
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"""
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def compress_jpg_png(filename, originpath):
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name = filename.rstrip('.png').rstrip('.jpg')
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im = Image.open(os.path.join(originpath, filename))
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# 解决图像方向问题
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try:
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for orientation in ExifTags.TAGS.keys():
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if ExifTags.TAGS[orientation] == 'Orientation': break
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exif = dict(im._getexif().items())
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if exif[orientation] == 3:
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im = im.rotate(180, expand=True)
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elif exif[orientation] == 6:
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im = im.rotate(270, expand=True)
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elif exif[orientation] == 8:
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im = im.rotate(90, expand=True)
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except:
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pass
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# print(im.format,im.size,im.mode)
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im = im.convert('RGB')
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im.format = "JPEG"
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new_photo = im.copy()
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new_photo.thumbnail(im.size, resample=Image.ANTIALIAS)
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save_args = {'format': im.format}
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# print(save_args)
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# if im.format=='JPEG':
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# save_args['quality']=20
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save_args['quality'], value = dynamic_quality.jpeg_dynamic_quality(im)
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save_args['optimize'] = True
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save_args['progressive=True'] = True
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# print("JPEG Quality Changed")
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# elif im.format=='PNG':
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# save_args['format']='JPEG'
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# save_args['quality']=5
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# print("PNG Quality Changed")
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new_file = os.path.join(originpath, name + str(int(time.time())) + ".jpg")
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new_photo.save(new_file, **save_args)
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return new_file
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if __name__ == '__main__':
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print(compress_jpg_png("1.jpg", "E:\\test"))
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@ -1,66 +0,0 @@
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import PIL.Image # 安装依赖包：pip3 install pillow
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from math import log
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from SSIM_PIL import compare_ssim # 安装依赖包：pip3 install SSIM-PIL
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def get_ssim_at_quality(photo, quality):
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"""Return the ssim for this JPEG image saved at the specified quality"""
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ssim_photo = "tmp.jpg"
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# optimize is omitted here as it doesn't affect
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# quality but requires additional memory and cpu
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photo.save(ssim_photo, format="JPEG", quality=quality, progressive=True)
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ssim_score = compare_ssim(photo, PIL.Image.open(ssim_photo))
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return ssim_score
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def _ssim_iteration_count(lo, hi):
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"""Return the depth of the binary search tree for this range"""
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if lo >= hi:
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return 0
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else:
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return int(log(hi - lo, 2)) + 1
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def jpeg_dynamic_quality(original_photo):
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"""Return an integer representing the quality that this JPEG image should be
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saved at to attain the quality threshold specified for this photo class.
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Args:
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original_photo - a prepared PIL JPEG image (only JPEG is supported)
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"""
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ssim_goal = 0.9 #the original value is 0.95
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hi = 35 #the original value is 85
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lo = 30 #the original value is 80
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# working on a smaller size image doesn't give worse results but is faster
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# changing this value requires updating the calculated thresholds
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photo = original_photo.resize((200, 200))
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# if not _should_use_dynamic_quality():
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# default_ssim = get_ssim_at_quality(photo, hi)
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# return hi, default_ssim
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# 95 is the highest useful value for JPEG. Higher values cause different behavior
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# Used to establish the image's intrinsic ssim without encoder artifacts
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normalized_ssim = get_ssim_at_quality(photo, 10)
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selected_quality = selected_ssim = None
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# loop bisection. ssim function increases monotonically so this will converge
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for i in range(_ssim_iteration_count(lo, hi)):
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curr_quality = (lo + hi) // 2
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curr_ssim = get_ssim_at_quality(photo, curr_quality)
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ssim_ratio = curr_ssim / normalized_ssim
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if ssim_ratio >= ssim_goal:
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# continue to check whether a lower quality level also exceeds the goal
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selected_quality = curr_quality
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selected_ssim = curr_ssim
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hi = curr_quality
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else:
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lo = curr_quality
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if selected_quality:
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return selected_quality, selected_ssim
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else:
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default_ssim = get_ssim_at_quality(photo, hi)
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return hi, default_ssim
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@ -9,7 +9,6 @@
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import datetime
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import os
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from pathlib import Path
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from aiopathlib import AsyncPath
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from fastapi import UploadFile
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from application.settings import TEMP_DIR
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@ -8,12 +8,10 @@
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import asyncio
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import io
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import os
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import shutil
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import zipfile
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from application.settings import STATIC_ROOT, BASE_DIR, STATIC_URL
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from fastapi import UploadFile
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import sys
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from pathlib import Path
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from core.exception import CustomException
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from utils.file.file_base import FileBase
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from aiopathlib import AsyncPath
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