def process():
input_path = generate_random_filename(upload_directory, "jpg")
output_path = generate_random_filename(result_directory, "jpg")
try:
url = request.json["url"]
download(url, input_path)
with tf.Session(config=tf.ConfigProto(
allow_soft_placement=True)) as sess:
gan = UGATIT(sess, args)
gan.build_model()
gan.test_endpoint_init()
gan.test_endpoint(input_path, output_path)
callback = send_file(output_path, mimetype='image/jpeg')
return callback, 200
except:
traceback.print_exc()
return {'message': 'input error'}, 400
finally:
clean_all([input_path, output_path])
def index():
# set session for image results
if "file_urls" not in session:
session['file_urls'] = []
# list to hold our uploaded image urls
file_urls = session['file_urls']
if request.method == 'POST':
file_obj = request.files
for f in file_obj:
file = request.files.get(f)
# convert string of image data to uint8
nparr = np.fromfile(file, np.uint8)
# decode image
img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
# parse arguments
args = parse_args()
if args is None:
exit()
# open session
with tf.Session(config=tf.ConfigProto(
allow_soft_placement=True)) as sess:
gan = UGATIT(sess, args)
# build graph
gan.build_model()
# show network architecture
show_all_variables()
# do some fancy processing here....
fake_img = gan.test_endpoint(img)
# save the file with to our photos folder
filename = str(uuid.uuid1()) + '.png'
cv2.imwrite('uploads/' + filename, fake_img)
# append image urls
file_urls.append(photos.url(filename))
session['file_urls'] = file_urls
return "uploading..."
return render_template('index.html')
def runner(args):
# open session
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
gan = UGATIT(sess, args)
# build graph
gan.build_model()
# show network architecture
show_all_variables()
gan.test_endpoint_init()
while True:
time.sleep(5)
messages = get_messages_from_queue()
total_msg = len(messages)
print("[INFO] Retrieved " + str(total_msg) + " messages")
# email service
email = EmailService()
for message in messages:
total_msg = total_msg - 1
print("[INFO] " + str(total_msg) + " messages remain in queue")
try:
try:
body = json.loads(message['Body'])
print(body)
bucket = body['bucket_name']
bucket_key = body['bucket_key']
bucket_cropped_key = body.get('bucket_cropped_key', None)
file_name = body['file_name']
email_addr = body['email']
crop = body['crop']
# Crop params
x = crop['x']
y = crop['y']
width = crop['width']
height = crop['height']
except Exception as e:
print()
print("ERROR: Parsing message")
print(e)
raise e
if bucket_cropped_key is None:
try:
image = download_image(bucket, bucket_key)
except Exception as e:
print("ERROR: Downloading Image")
print(e)
raise e
try:
crop_img = image[y:y + height, x:x + width]
# Change color space
crop_img = cv2.cvtColor(crop_img, cv2.COLOR_RGB2BGR)
except Exception as e:
print("ERROR: Cropping Image")
print(e)
raise e
try:
# Resize image
crop_img = cv2.resize(crop_img, dsize=(256, 256))
except Exception as e:
print("ERROR: Resizing Image")
print(e)
raise e
else:
try:
crop_img = download_image(bucket, bucket_cropped_key)
crop_img = cv2.cvtColor(crop_img, cv2.COLOR_RGB2BGR)
except Exception as e:
print("ERROR: Downloading Cropped Image")
print(e)
raise e
try:
# do some fancy processing here....
fake_img = gan.test_endpoint(crop_img)
except Exception as e:
print("ERROR: Prosccing image with GAN")
print(e)
raise e
try:
# Upload to S3
image_url = upload_image(fake_img, file_name)
except Exception as e:
print("ERROR: Uploading image to S3")
print(e)
raise e
try:
# Send Email
email.send_email(email_addr, image_url)
except Exception as e:
print("ERROR: Failed to send email")
print(e)
raise e
except Exception as e:
print('FATAL ERROR')
print(e)