def predict_as_post():
args = configure_args()
model = load_model(args)
data = request.get_json()
img_frame = np.array(data['img'])
x, mask = pre_process_post(img_frame, args)
prob = np.squeeze(model.predict_on_batch([x, mask]))
prob_class = np.argmax(prob)
return jsonify(prob_class=int(prob_class),
negative_prob=float(prob[0]),
positive_prob=float(prob[1]))
import boto3
import pickle
import cv2
from model.opts import configure_args
from model.utils import set_logger, pre_process
import numpy as np
from model.network_architecture import create_model
import tensorflow as tf
if __name__ == "__main__":
user_id = 4642
args = configure_args()
set_logger('output/train_{}.log'.format(args.name))
s3 = boto3.client('s3')
response = s3.get_object(Bucket='cureskin-dataset',
Key='new_data/image_{}.pkl'.format(user_id))
body = response['Body'].read()
img_frame = pickle.loads(body)
x, mask = pre_process(img_frame, args)
print(mask)
checkpoint_path = 'ckpts'.format(args.name) + '/cp-0005.ckpt'
model = create_model(args)
model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
optimizer=tf.keras.optimizers.SGD(),
metrics=['accuracy',
tf.keras.metrics.Precision()])
model.load_weights(checkpoint_path)
prob = model.predict_on_batch([x, mask])