Performance Analysis of Bytes IO vs File IO

perf_bytes_io.py

"""
Usage:
>>> ipython
>>> %load perf_bytes_io.py
>>> <ENTER>
"""
import logging
import tempfile

import cv2
import requests

url = 'https://chaudhary.page.link/test-zomato-img'  # A jpeg image
response_object = requests.get(url)

def log(s):
    # print(s)
    pass

def file_io():
    import logging
    import os
    import tempfile

    import cv2
    import requests

    def download_image(url):
        #logging.info('Downloading image from url: %s', url[:100])
        #response_object = requests.get(url)
        file_descriptor, filename = tempfile.mkstemp(prefix='image-', suffix='.jpg')
        logging.info('Saving file: %s', filename)
        with open(file_descriptor, mode='wb') as f:
            f.write(response_object.content)
        return filename

    url = 'https://chaudhary.page.link/test-zomato-img'
    image_path = download_image(url)

    img = cv2.imread(image_path)
    resized_img = cv2.resize(img, (299, 299))
    # preprocess(resized_image)
    # prediction_score = model.predict(resized_img)
    os.remove(image_path)
    log(resized_img.shape)

def bytes_io():
    from io import BytesIO

    import cv2
    import numpy as np
    import requests


    url = 'https://chaudhary.page.link/test-zomato-img'
    #response_object = requests.get(url)
    image_data = BytesIO(response_object.content)
    file_bytes = np.asarray(bytearray(image_data.read()), dtype=np.uint8)
    img = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
    image_data.close()
    resized_img = cv2.resize(img, (299, 299))
    # preprocess(resized_image)
    # prediction_score = model.predict(resized_img)
    log(resized_img.shape)

def direct_decode():
    import cv2
    import numpy as np
    import requests


    url = 'https://chaudhary.page.link/test-zomato-img'
    #response_object = requests.get(url)
    file_bytes = np.asarray(bytearray(response_object.content), dtype=np.uint8)
    img = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
    resized_img = cv2.resize(img, (299, 299))
    # preprocess(resized_image)
    # prediction_score = model.predict(resized_img)
    log(resized_img.shape)

%timeit file_io()
%timeit bytes_io()
%timeit direct_decode()