import os
import base64
import json
import logging
import urllib
from io import BytesIO
import sys
import requests
import toolz
from PIL import Image, ImageOps
ch = logging.StreamHandler(sys.stdout)
ch.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
ch.setFormatter(formatter)
logging.basicConfig(level=logging.INFO, stream=sys.stdout)
logger = logging.getLogger(__name__)
# logger.addHandler(ch)
TEST_IMAGES ={ # Examples used for testing
'https://www.britishairways.com/assets/images/information/about-ba/fleet-facts/airbus-380-800/photo-gallery/240x295-BA-A380-exterior-2-high-res.jpg':
'n02690373 airliner',
}
def read_image_from(url):
return toolz.pipe(url,
urllib.request.urlopen,
lambda x: x.read(),
BytesIO)
def to_rgb(img_bytes):
return Image.open(img_bytes).convert('RGB')
@toolz.curry
def resize(img_file, new_size=(100, 100)):
return ImageOps.fit(img_file, new_size, Image.ANTIALIAS)
def to_base64(img):
imgio = BytesIO()
img.save(imgio, 'PNG')
imgio.seek(0)
dataimg = base64.b64encode(imgio.read())
return dataimg.decode('utf-8')
def to_img(img_url):
return toolz.pipe(img_url,
read_image_from,
to_rgb,
resize(new_size=(224,224)))
def img_url_to_json(url):
img_data = toolz.pipe(url,
to_img,
to_base64)
return json.dumps({'input':'[\"{0}\"]'.format(img_data)})
if __name__=='__main__':
logger.info('Starting classifier test')
headers = {'content-type': 'application/json'}
for url, label in TEST_IMAGES.items():
jsonimg = img_url_to_json(url)
r = requests.post(os.environ.get('MODEL_API_URL', "http://0.0.0.0:88").rstrip('/') + "/score", data=jsonimg, headers=headers)
json_response = r.json()
logger.info(json_response)
prediction=json_response['result'][0][0][0][0]
if prediction != label:
raise ValueError('The predicted label {} is not the same as {}'.format(prediction,label))
logger.info('CORRECT! {}'.format(prediction))
