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app.py
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app.py
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import io
import os
import sys
import shutil
import uuid
import copy
import threading
import time
from queue import Empty, Queue
from flask import Flask, render_template, request, jsonify, send_file
from munch import Munch
from werkzeug.utils import secure_filename
from torch.backends import cudnn
import torch
from facenet_pytorch import MTCNN
from PIL import Image
import numpy as np
from core.data_loader import get_test_loader
from core.solver import Solver
from core.wing import align_faces
from main import parse_args
if torch.cuda.is_available():
pass
else:
sys.exit('Cuda is not available')
#preload model
def create_model(args, model_type):
args.resume_iter = 100000
if model_type == 'CelebA-HQ':
args.num_domains = 2
args.w_hpf = 1
args.checkpoint_dir = 'expr/checkpoints/celeba_hq'
else:
args.num_domains = 3
args.w_hpf = 0
args.checkpoint_dir = 'expr/checkpoints/afhq'
model = Solver(args)
return model
#update dir
def update_args(args, f_id):
args.inp_dir = os.path.join(UPLOAD_FOLDER, f_id)
args.out_dir = os.path.join(TARGET_FOLDER, f_id, f_id)
args.src_dir = os.path.join(TARGET_FOLDER, f_id)
args.result_dir = os.path.join(RESULT_FOLDER, f_id)
return args
#remove image data
def remove_image(args):
shutil.rmtree(args.inp_dir)
shutil.rmtree(args.src_dir)
shutil.rmtree(args.result_dir)
#detect face in image
def detect_face(im):
sys.stderr.write("Detecting face using MTCNN face detector")
try:
bboxes, prob = face_detector.detect(im)
w0, h0, w1, h1 = bboxes[0]
except Exception as e:
print(e)
sys.stderr.write("Could not detect faces in the image")
return False
###
w_crop = (w1 - w0) / 3
h_crop = (h1 - h0) / 3
if w0 - w_crop > 0:
w0 -= w_crop
else:
w0 = 0
if w1 + w_crop < im.shape[1]:
w1 += w_crop
else:
w1 = im.shape[1]
if h0 - h_crop > 0:
h0 -= h_crop
else:
h0 = 0
if h1 + h_crop < im.shape[0]:
h1 += h_crop
else:
h1 = im.shape[0]
###
return im[int(h0):int(h1), int(w0):int(w1)]
#########################################################
UPLOAD_FOLDER = 'img_data/upload'
TARGET_FOLDER = 'img_data/target'
RESULT_FOLDER = 'img_data/result'
cudnn.benchmark = True
default_args = parse_args()
CelebA_HQ = create_model(copy.copy(default_args), 'CelebA-HQ')
AFHQ = create_model(copy.copy(default_args), 'AFHQ')
face_detector = MTCNN(select_largest=True, device=torch.device('cuda'))
requests_queue = Queue()
#########################################################
app = Flask(__name__, template_folder="./static/")
app.config['MAX_CONTENT_LENGTH'] = 1 * 1024 * 1024
BATCH_SIZE=1
CHECK_INTERVAL=0.1
#run model
def run(input_file, model_type):
f_id = str(uuid.uuid4())
fname = secure_filename(input_file.filename)
# save image to upload folder
os.makedirs(os.path.join(UPLOAD_FOLDER, f_id), exist_ok=True)
#update args
args = update_args(default_args, f_id)
torch.manual_seed(args.seed)
#allocate solver and update args.ref_dir
if model_type == "Human Face":
solver = CelebA_HQ
args.ref_dir = 'assets/representative/celeba_hq/ref'
# human face crop
pil_im = Image.open(input_file.stream).convert('RGB')
im = np.uint8(pil_im)
face_im = detect_face(copy.copy(im))
# if can not detect face
if type(face_im) == bool:
return 'no face'
Image.fromarray(face_im).save(os.path.join(UPLOAD_FOLDER, f_id, fname))
else:
solver = AFHQ
args.ref_dir = 'assets/representative/afhq/ref'
input_file.save(os.path.join(UPLOAD_FOLDER, f_id, fname))
# align image
align_faces(args, args.inp_dir, args.out_dir)
#define loaders
loaders = Munch(src=get_test_loader(root=args.src_dir,
img_size=args.img_size,
batch_size=args.val_batch_size,
shuffle=False,
num_workers=args.num_workers),
ref=get_test_loader(root=args.ref_dir,
img_size=args.img_size,
batch_size=args.val_batch_size,
shuffle=False,
num_workers=args.num_workers))
#generate image
solver.sample(loaders, args.result_dir)
#read image
path = os.path.join(args.result_dir, 'reference.jpg')
with open(path, 'rb') as f:
data = f.read()
result = io.BytesIO(data)
#remove image data
remove_image(args)
return result
def handle_requests_by_batch():
try:
while True:
requests_batch = []
while not (
len(requests_batch) >= BATCH_SIZE # or
#(len(requests_batch) > 0 #and time.time() - requests_batch[0]['time'] > BATCH_TIMEOUT)
):
try:
requests_batch.append(requests_queue.get(timeout=CHECK_INTERVAL))
except Empty:
continue
batch_outputs = []
for request in requests_batch:
batch_outputs.append(run(request['input'][0], request['input'][1]))
for request, output in zip(requests_batch, batch_outputs):
request['output'] = output
except Exception as e:
while not requests_queue.empty():
requests_queue.get()
print(e)
threading.Thread(target=handle_requests_by_batch).start()
@app.route('/predict', methods=['POST'])
def predict():
try :
if requests_queue.qsize() >= 1:
return jsonify({'message': 'Too Many Requests! Please retry request.'}), 429
model_type = request.form['check_model']
input_file = request.files['source']
if input_file.content_type not in ['image/jpeg', 'image/jpg', 'image/png']:
return jsonify({'message': 'Only support jpeg, jpg or png'}), 400
req = {
'input': [input_file, model_type]
}
requests_queue.put(req)
while 'output' not in req:
time.sleep(CHECK_INTERVAL)
result = req['output']
if result == 'no face':
return jsonify({'message': 'Could not detect faces in the image'}), 400
return send_file(result, mimetype='image/jpeg')
except :
return jsonify({'message': 'Error occurred on server!'}), 500
@app.route('/health')
def health():
return "ok"
@app.route('/')
def main():
return render_template('index.html')
if __name__ == "__main__":
from waitress import serve
serve(app, port=80, host='0.0.0.0')