代码核心思路
循环降低图片质量quality直至图片质量大小符合目标质量后退出循环
pillow压缩失真严重
'''
pip install pillow
'''
from io import BytesIO
from PIL import Image
def pic_compress(pic_path, out_path, target_size, quality=90, step=5):
# 读取图片bytes
with open(pic_path, 'rb') as f:
pic_byte = f.read()
current_size = len(pic_byte) / 1024 # 图片当前质量大小
print("图片压缩前的大小为(KB)：", current_size)
while current_size > target_size: # 当达到目标压缩大小后退出循环
img = Image.open(BytesIO(pic_byte))
buf = BytesIO()
img.save(buf, format=img.format, quality=quality)
pic_byte = buf.getvalue()
if quality - step < 0: # 以降低到最小质量
break
quality -= step
current_size = len(pic_byte) / 1024
# 保存图片
with open(out_path, 'wb') as f:
f.write(pic_byte)
return len(pic_byte) / 1024
def main():
pic_size = pic_compress('test.jpg', 'new_test.jpg', target_size=100)
print("图片压缩后的大小为(KB)：", pic_size)
if __name__ == '__main__':
main()
pillow处理后和原图片的对比
使用pillow压缩的图片不单止大小质量没达标而且处理后的图片失真不忍直视。
使用opencv保真压缩
'''
pip install numpy
pip install opencv-python
'''
from io import BytesIO
import cv2
import numpy as np
def pic_compress(pic_path, out_path, target_size=199, quality=90, step=5, pic_type='.jpg'):
# 读取图片bytes
with open(pic_path, 'rb') as f:
pic_byte = f.read()
img_np = np.frombuffer(pic_byte, np.uint8)
img_cv = cv2.imdecode(img_np, cv2.IMREAD_ANYCOLOR)
current_size = len(pic_byte) / 1024
print("图片压缩前的大小为(KB)：", current_size)
while current_size > target_size:
pic_byte = cv2.imencode(pic_type, img_cv, [int(cv2.IMWRITE_JPEG_QUALITY), quality])[1]
if quality - step < 0:
break
quality -= step
current_size = len(pic_byte) / 1024
# 保存图片
with open(out_path, 'wb') as f:
f.write(BytesIO(pic_byte).getvalue())
return len(pic_byte) / 1024
def main():
pic_size = pic_compress('test.jpg', 'new_test.jpg', target_size=100)
print("图片压缩后的大小为(KB)：", pic_size)
if __name__ == '__main__':
main()
opencv处理后和原图片的对比
文章灵感
想用python压缩图片然后网上搜，搜索结果90%都是python使用pillow的压缩。最主要是pillow压缩后失真严重，在图片压缩方面纯属是个摆设的功能。
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