Comparison of Python image reading and writing methods

Qi Zhou 2020-11-14 22:39:13
comparison python image reading writing


When training the neural network model of vision correlation , It's always about reading and writing images . There are many ways , such as matplotlib、cv2、PIL etc. . Here are some ways to read and write , To choose the fastest way , Improve your training speed .

Experimental standards

Because the training framework is Pytorch, So the experimental criteria for reading are as follows :

1、 The reading resolution is 1920x1080 Of 5 A picture (png Format a piece of ,jpg Format: 4 sheets ) And save it to the array .

2、 Convert the read array to dimensions in the order of CxHxW Of Pytorch tensor , And save it to video memory ( I use GPU Training ), The order of the three channels is RGB.

3、 Record the time spent by each method in the above operations . because png The size of the image in the format is almost the same as the quality jpg Format 10 times , So datasets don't usually use png To preserve , Do not compare the reading time difference between the two formats .

The experimental criteria are as follows :

1、 take 5 Zhang 1920x1080 Of 5 The image corresponds to Pytorch The tensor is converted to an array of data types that can be used by the corresponding method .

2、 With jpg Format saves five pictures .

3、 Record the time taken by each method to save the picture .

The experiment

cv2

Because there is GPU, therefore cv2 There are two ways to read pictures :

1、 First read all the pictures as one numpy Array , And then convert it to save it in GPU Medium pytorch tensor .

2、 Initialize a save in GPU Medium pytorch tensor , And then copy each graph directly into this tensor .

The first way is as follows :

import os, torch
import cv2 as cv
import numpy as np
from time import time
read_path = 'D:test'
write_path = 'D:test\\write\\'
# cv2 Read 1
start_t = time()
imgs = np.zeros([5, 1080, 1920, 3])
for img, i in zip(os.listdir(read_path), range(5)):
img = cv.imread(filename=os.path.join(read_path, img))
imgs[i] = img
imgs = torch.tensor(imgs).to('cuda')[...,[2,1,0]].permute([0,3,1,2])/255
print('cv2 Read time 1:', time() - start_t)
# cv2 preservation 
start_t = time()
imgs = (imgs.permute([0,2,3,1])[...,[2,1,0]]*255).cpu().numpy()
for i in range(imgs.shape[0]):
cv.imwrite(write_path + str(i) + '.jpg', imgs[i])
print('cv2 Storage time :', time() - start_t) 

experimental result :

cv2 Read time 1: 0.39693760871887207
cv2 Storage time : 0.3560612201690674

In the second way, the code is as follows :

import os, torch
import cv2 as cv
import numpy as np
from time import time
read_path = 'D:test'
write_path = 'D:test\\write\\'
# cv2 Read 2
start_t = time()
imgs = torch.zeros([5, 1080, 1920, 3], device='cuda')
for img, i in zip(os.listdir(read_path), range(5)):
img = torch.tensor(cv.imread(filename=os.path.join(read_path, img)), device='cuda')
imgs[i] = img
imgs = imgs[...,[2,1,0]].permute([0,3,1,2])/255
print('cv2 Read time 2:', time() - start_t)
# cv2 preservation 
start_t = time()
imgs = (imgs.permute([0,2,3,1])[...,[2,1,0]]*255).cpu().numpy()
for i in range(imgs.shape[0]):
cv.imwrite(write_path + str(i) + '.jpg', imgs[i])
print('cv2 Storage time :', time() - start_t) 

experimental result :

cv2 Read time 2: 0.23636841773986816
cv2 Storage time : 0.3066873550415039

matplotlib

The same two ways to read , The first code is as follows :

import os, torch
import numpy as np
import matplotlib.pyplot as plt
from time import time
read_path = 'D:test'
write_path = 'D:test\\write\\'
# matplotlib Read 1
start_t = time()
imgs = np.zeros([5, 1080, 1920, 3])
for img, i in zip(os.listdir(read_path), range(5)):
img = plt.imread(os.path.join(read_path, img))
imgs[i] = img
imgs = torch.tensor(imgs).to('cuda').permute([0,3,1,2])/255
print('matplotlib Read time 1:', time() - start_t)
# matplotlib preservation 
start_t = time()
imgs = (imgs.permute([0,2,3,1])).cpu().numpy()
for i in range(imgs.shape[0]):
plt.imsave(write_path + str(i) + '.jpg', imgs[i])
print('matplotlib Storage time :', time() - start_t) 

experimental result :

matplotlib Read time 1: 0.45380306243896484
matplotlib Storage time : 0.768944263458252

The second way to experiment with code :

import os, torch
import numpy as np
import matplotlib.pyplot as plt
from time import time
read_path = 'D:test'
write_path = 'D:test\\write\\'
# matplotlib Read 2
start_t = time()
imgs = torch.zeros([5, 1080, 1920, 3], device='cuda')
for img, i in zip(os.listdir(read_path), range(5)):
img = torch.tensor(plt.imread(os.path.join(read_path, img)), device='cuda')
imgs[i] = img
imgs = imgs.permute([0,3,1,2])/255
print('matplotlib Read time 2:', time() - start_t)
# matplotlib preservation 
start_t = time()
imgs = (imgs.permute([0,2,3,1])).cpu().numpy()
for i in range(imgs.shape[0]):
plt.imsave(write_path + str(i) + '.jpg', imgs[i])
print('matplotlib Storage time :', time() - start_t) 

experimental result :

matplotlib Read time 2: 0.2044532299041748
matplotlib Storage time : 0.4737534523010254

It should be noted that ,matplotlib Read png Format image to get the array value is in $[0, 1]$ Range of floating point Numbers , and jpg The format picture is in $[0, 255]$ Range of integers . So if the image format in the dataset is inconsistent , Be careful to convert to consistent before reading , Otherwise, the preprocessing of data sets will be troublesome .

PIL

PIL Can't be used directly pytorch Tensor or numpy Array , First convert to Image type , So it's a lot of trouble , Time complexity must have been the underdog , No more experiments .

torchvision

torchvision Provides direct access from pytorch The function of tensor to save pictures , And the fastest read above matplotlib A combination of methods , The code is as follows :

import os, torch
import matplotlib.pyplot as plt
from time import time
from torchvision import utils
read_path = 'D:test'
write_path = 'D:test\\write\\'
# matplotlib Read 2
start_t = time()
imgs = torch.zeros([5, 1080, 1920, 3], device='cuda')
for img, i in zip(os.listdir(read_path), range(5)):
img = torch.tensor(plt.imread(os.path.join(read_path, img)), device='cuda')
imgs[i] = img
imgs = imgs.permute([0,3,1,2])/255
print('matplotlib Read time 2:', time() - start_t)
# torchvision preservation 
start_t = time()
for i in range(imgs.shape[0]):
utils.save_image(imgs[i], write_path + str(i) + '.jpg')
print('torchvision Storage time :', time() - start_t) 

experimental result :

matplotlib Read time 2: 0.15358829498291016
torchvision Storage time : 0.14760661125183105

You can see that these two are the fastest ways to read and write . in addition , Try to make the reading and writing of pictures not affect the training process , We can also run these two processes in parallel with training .

版权声明
本文为[Qi Zhou]所创,转载请带上原文链接,感谢

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