print("%dth frame has been processed" % frame_count)
frame_count += 1
except Exception as e:
print("video has been prcessed to frames")
cap.release()
print("start load models")
# load models
detector = MTCNN()
device = torch.device('cuda')
G = AEI_Net(c_id=512)
G.eval()
G.load_state_dict(
torch.load('./saved_models/G_latest.pth',
map_location=torch.device('cpu')))
G = G.cuda()
arcface = Backbone(50, 0.6, 'ir_se').to(device)
arcface.eval()
arcface.load_state_dict(torch.load('./face_modules/model_ir_se50.pth',
map_location=device),
strict=False)
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
# load source image
Xs_raw = cv2.imread(source_image_path)
Xs = detector.align(Image.fromarray(Xs_raw[:, :, ::-1]), crop_size=(256, 256))
def serve():
data = {"success": False}
detector = MTCNN()
device = torch.device('cuda')
G = AEI_Net(c_id=512)
G.eval()
G.load_state_dict(
torch.load('./saved_models/G_latest.pth',
map_location=torch.device('cpu')))
G = G.cuda()
arcface = Backbone(50, 0.6, 'ir_se').to(device)
arcface.eval()
arcface.load_state_dict(torch.load('./face_modules/model_ir_se50.pth',
map_location=device),
strict=False)
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
if flask.request.method == 'POST':
st = time.time()
metadata = flask.request.form
source_image = metadata['source_image']
target_image = metadata['target_image']
source_image = base64.b64decode(source_image.encode('utf-8'))
Xs_raw = cv2.imdecode(np.frombuffer(source_image, np.uint8),
cv2.IMREAD_COLOR)
target_image = base64.b64decode(target_image.encode('utf-8'))
Xt_raw = cv2.imdecode(np.frombuffer(target_image, np.uint8),
cv2.IMREAD_COLOR)
Xs = detector.align(Image.fromarray(Xs_raw[:, :, ::-1]),
crop_size=(256, 256))
Xs_raw = np.array(Xs)[:, :, ::-1]
Xs = test_transform(Xs)
Xs = Xs.unsqueeze(0).cuda()
with torch.no_grad():
embeds = arcface(
F.interpolate(Xs[:, :, 19:237, 19:237], (112, 112),
mode='bilinear',
align_corners=True))
Xt, trans_inv = detector.align(Image.fromarray(Xt_raw[:, :, ::-1]),
crop_size=(256, 256),
return_trans_inv=True)
Xt_raw = Xt_raw.astype(np.float) / 255.0
Xt = test_transform(Xt)
Xt = Xt.unsqueeze(0).cuda()
mask = np.zeros([256, 256], dtype=np.float)
for i in range(256):
for j in range(256):
dist = np.sqrt((i - 128)**2 + (j - 128)**2) / 128
dist = np.minimum(dist, 1)
mask[i, j] = 1 - dist
mask = cv2.dilate(mask, None, iterations=20)
with torch.no_grad():
Yt, _ = G(Xt, embeds)
Yt = Yt.squeeze().detach().cpu().numpy().transpose([1, 2, 0
]) * 0.5 + 0.5
Yt = Yt[:, :, ::-1]
Yt_trans_inv = cv2.warpAffine(
Yt,
trans_inv, (np.size(Xt_raw, 1), np.size(Xt_raw, 0)),
borderValue=(0, 0, 0))
mask_ = cv2.warpAffine(mask,
trans_inv,
(np.size(Xt_raw, 1), np.size(Xt_raw, 0)),
borderValue=(0, 0, 0))
mask_ = np.expand_dims(mask_, 2)
Yt_trans_inv = mask_ * Yt_trans_inv + (1 - mask_) * Xt_raw
img_data = Yt_trans_inv * 255
retval, buffer = cv2.imencode('.jpg', img_data)
pic_str = base64.b64encode(buffer)
pic_str = pic_str.decode()
data['success'] = True
data['image'] = pic_str
st = time.time() - st
print(f'process time: {st} sec')
return flask.jsonify(data)