python - 将PIL图像转换为base64的更快方法

这是我将PIL图像转换为base64的功能：

# input: single PIL image
def image_to_base64(self, image):
    output_buffer = BytesIO()

    now_time = time.time()
    image.save(output_buffer, format='PNG')
    print('--image.save:' + str(time.time()-now_time))

    now_time = time.time()
    byte_data = output_buffer.getvalue()
    print('--output_buffer.getvalue:' + str(time.time()-now_time))

    now_time = time.time()
    encoded_input_string  = base64.b64encode(byte_data)
    print('--base64.b64encode:' + str(time.time()-now_time))

    now_time = time.time()
    input_string = encoded_input_string.decode("utf-8")
    print('--encoded_input_string.decode:' + str(time.time()-now_time))

    return input_string

我的输出：


  --image.save:1.05138802528
  
  --output_buffer.getvalue：0.000611066818237
  
  --base64.b64编码：0.01047706604
  
  --encoded_input_string.decode：0.0172328948975


如我们所见，该函数在速度上很慢。我们该怎样改进这个？

[编辑]

好！这是完整的例子

import time
import requests
import base64
from PIL import Image
from io import BytesIO


# input: single PIL image
def image_to_base64(image):
    output_buffer = BytesIO()

    now_time = time.time()
    image.save(output_buffer, format='PNG')
    print('--image.save:' + str(time.time()-now_time))

    now_time = time.time()
    byte_data = output_buffer.getvalue()
    print('--output_buffer.getvalue:' + str(time.time()-now_time))

    now_time = time.time()
    encoded_input_string  = base64.b64encode(byte_data)
    print('--base64.b64encode:' + str(time.time()-now_time))

    now_time = time.time()
    input_string = encoded_input_string.decode("utf-8")
    print('--encoded_input_string.decode:' + str(time.time()-now_time))

    return input_string

img_url = "https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2015/07/stuttgart03.png"
response = requests.get(img_url)
img = Image.open(BytesIO(response.content))
input_string = image_to_base64(img)

这里的瓶颈是

image.save(output_buffer, format='PNG')

将PIL图像转换为字节。我认为如果可以加快这一步会很好。

最佳答案

如评论中所建议，我尝试了pyvips如下：

#!/usr/bin/env python3
import requests
import base64
import numpy as np
from PIL import Image
from io import BytesIO
from cv2 import imencode
import pyvips

def vips_2PNG(image,compression=6):
    # Convert PIL Image to Numpy array
    na = np.array(image)
    height, width, bands = na.shape

    # Convert Numpy array to Vips image
    dtype_to_format = {
       'uint8': 'uchar',
       'int8': 'char',
       'uint16': 'ushort',
       'int16': 'short',
       'uint32': 'uint',
       'int32': 'int',
       'float32': 'float',
       'float64': 'double',
       'complex64': 'complex',
       'complex128': 'dpcomplex',
    }
    linear = na.reshape(width * height * bands)
    vi = pyvips.Image.new_from_memory(linear.data, width, height, bands,dtype_to_format[str(na.dtype)])

    # Save to memory buffer as PNG
    data = vi.write_to_buffer(f".png[compression={compression}]")
    return data

def vips_including_reading_from_disk(image):
    # Load image from disk
    image = pyvips.Image.new_from_file('stuttgart.png', access='sequential')
    # Save to memory buffer as PNG
    data = image.write_to_buffer('.png')
    return data

def faster(image):
    image_arr = np.array(image)
    _, byte_data = imencode('.png', image_arr)
    return byte_data

def orig(image, faster=True):
    output_buffer = BytesIO()
    image.save(output_buffer, format='PNG')
    byte_data = output_buffer.getvalue()
    return byte_data

# img_url = "https://www.cityscapes-dataset.com/wordpress/wp-content/uploads/2015/07/stuttgart03.png"
filename = 'stuttgart.png'
img = Image.open(filename)

# r = orig(img)
# print(len(r))
# %timeit r = orig(img)

# r = faster(img)
# print(len(r))
# %timeit r = faster(img)

# r = vips_including_reading_from_disk(filename)
# print(len(r))
# %timeit r = vips_including_reading_from_disk(filename)

# r = vips_2PNG(img,0)
# print(len(r))
# %timeit r = vips_2PNG(img,0)

我正在考虑在文件大小和速度之间折衷选择compression参数。这就是我得到的-我不会比较绝对值，而是查看机器上相对于彼此的性能：

               Filesize        Time
PIL            1.7MB           1.12s
OpenCV         2.0MB           173ms   <--- COMPARE
vips(comp=0)   6.2MB           66ms
vips(comp=1)   2.0MB           132ms   <--- COMPARE
vips(comp=2)   2.0MB           153ms

我将箭头放在要比较的箭头旁边。

关于python - 将PIL图像转换为base64的更快方法，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/58705700/