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cloud_apis.py
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import urllib
import boto3
import time
from PIL import Image
import datetime
import json
import base64
from io import BytesIO
import Algorithmia
class Baidu(object):
def __init__(self,
AK='gVdv****************NWA',
SK='OYa*********************HKc8'):
self.api_name = "baidu"
self.ak = AK
self.sk = SK
self.token = self._get_access_token()
def _get_access_token(self):
host = 'https://aip.baidubce.com/oauth/2.0/token?grant_type=client_credentials&client_id=%s&client_secret=%s' % \
(self.ak, self.sk)
request = urllib.request.Request(host)
request.add_header('Content-Type', 'application/json; charset=UTF-8')
response = urllib.request.urlopen(request)
content = response.read().decode()
return json.loads(content)['access_token']
def _base64_encode(self, image, quality):
f = BytesIO()
image.save(f, format='jpeg', quality=quality)
binary_data = f.getvalue()
f.seek(0)
size = len(f.getvalue())
return base64.b64encode(binary_data), size
def recognize(self, image, quality):
time.sleep(0.1)
img_b64, size = self._base64_encode(image, quality)
request_url = "https://aip.baidubce.com/rest/2.0/image-classify/v2/advanced_general"
params = {"image": img_b64}
params = urllib.parse.urlencode(params).encode(encoding='UTF8')
access_token = self.token
request_url = request_url + "?access_token=" + access_token
request = urllib.request.Request(url=request_url, data=params)
request.add_header('Content-Type', 'application/x-www-form-urlencoded')
response = urllib.request.urlopen(request)
content = response.read().decode()
response_dict = json.loads(content)
if not 'error_code' in response_dict.keys():
return 0, response_dict['result'], size
else:
print(response_dict['error_msg'])
return 1, [response_dict['error_msg']], size
class FacePP(Baidu):
def __init__(self, AK='kf***********************6Mr3', SK='ta0*********************DLP'):
self.api_name = "face_plusplus"
self.ak = AK
self.sk = SK
def recognize(self, image, quality):
time.sleep(0.1)
img_b64, size = self._base64_encode(image, quality)
request_url = "https://api-cn.faceplusplus.com/imagepp/beta/detectsceneandobject"
params = {"api_key": self.ak,
"api_secret": self.sk,
"image_base64": img_b64}
params = urllib.parse.urlencode(params).encode(encoding="UTF-8")
request = urllib.request.Request(url=request_url, data=params)
try:
response = urllib.request.urlopen(request)
content = response.read().decode()
response_dict = json.loads(content)
if not "error_message" in response_dict.keys():
# print(response_dict['time_used'])
if len(response_dict['objects']) == 0:
return 2, [{"keyword": "", "score": 1e-6}], size
result_dicts = [{"keyword": line_dict['value'], "score": line_dict['confidence'] / 100.} for line_dict
in response_dict['objects']]
return 0, result_dicts, size
else:
print(response_dict['error_message'])
return 1, [response_dict['error_message']], size
except Exception as e:
print(e)
return 3, [{"keyword": "", "score": 1e-6}], size
class AlgorithmiaAPI(Baidu):
def __init__(self, AK='sim************************nEB61'):
self.client = Algorithmia.client(AK)
self.api_name = "Algorithmia"
def upload(self, image, quality):
file_location = "data://hosea1008/classification_images/%s.jpg" % datetime.datetime.now().strftime(
'%Y-%m-%d_%H:%M:%S.%f')
datafile = self.client.file(file_location)
f = BytesIO()
image.save(f, format='jpeg', quality=quality)
size = len(f.getvalue())
f.seek(0)
datafile.client.putHelper(datafile.url, f)
return file_location, size
def recognize(self, image, quality):
file_location, size = self.upload(image, quality)
algo = self.client.algo('yavuzkomecoglu/ImageClassification/0.1.0')
try:
result = algo.pipe({"image": file_location}).result
if len(result['predictions']) == 0:
return 2, [{"keyword": "", "score": 1e-6}], size
else:
return 0, [{"keyword": line_dict['label'], "score": line_dict['probability']} for line_dict in
result['predictions']], size
except Exception as e:
print(e)
return 1, [e], size
class AmazonRekognition(Baidu):
def __init__(self):
self.s3 = boto3.client('s3',
aws_access_key_id='AKIA*************672Q',
aws_secret_access_key='65Y**************************tMr')
self.recognizer = boto3.client('rekognition',
aws_access_key_id='AKIA**************672Q',
aws_secret_access_key='65Y*************************tMr',
region_name='ap-northeast-2'
)
self.bucket_name = 'fastinference-images-korea'
self.api_name = 'amazon'
def upload(self, image, quality):
f = BytesIO()
image.save(f, format='jpeg', quality=quality)
size = len(f.getvalue())
f.seek(0)
self.s3.put_object(
Bucket=self.bucket_name,
Key='test5.jpeg',
Body=f,
ContentType='image/jpeg'
)
return size
def recognize(self, image, quality):
size = self.upload(image, quality)
try:
result = self.recognizer.detect_labels(
Image={'S3Object': {'Bucket': self.bucket_name, 'Name': 'test5.jpeg'}}, MaxLabels=10)
if len(result['Labels']) == 0:
return 2, [{"keyword": "", "score": 1e-6}], size
else:
return 0, [{"keyword": line_dict['Name'], "score": line_dict['Confidence'] / 100.} for line_dict in
result['Labels']], size
except Exception as e:
print(e)
return 1, [e], size
if __name__ == '__main__':
amazon = AmazonRekognition()
image = Image.open('/home/hsli/imagenet-data/train/n03085013/n03085013_773.JPEG')
amazon.recognize(image, 75)