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import cog
import cv2
import tempfile
import torch
import numpy as np
import os
from pathlib import Path
from utils import Preprocess
from models import ResnetGenerator
class Predictor(cog.Predictor):
def setup(self):
pass
@cog.input("photo", type=Path, help="portrait photo (size < 1M)")
def predict(self, photo):
img = cv2.cvtColor(cv2.imread(str(photo)), cv2.COLOR_BGR2RGB)
out_path = gen_cartoon(img)
return out_path
def gen_cartoon(img):
pre = Preprocess()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
net = ResnetGenerator(ngf=32, img_size=256, light=True).to(device)
assert os.path.exists(
'./models/photo2cartoon_weights.pt'), "[Step1: load weights] Can not find 'photo2cartoon_weights.pt' in folder 'models!!!'"
params = torch.load('./models/photo2cartoon_weights.pt', map_location=device)
net.load_state_dict(params['genA2B'])
# face alignment and segmentation
face_rgba = pre.process(img)
if face_rgba is None:
return None
face_rgba = cv2.resize(face_rgba, (256, 256), interpolation=cv2.INTER_AREA)
face = face_rgba[:, :, :3].copy()
mask = face_rgba[:, :, 3][:, :, np.newaxis].copy() / 255.
face = (face * mask + (1 - mask) * 255) / 127.5 - 1
face = np.transpose(face[np.newaxis, :, :, :], (0, 3, 1, 2)).astype(np.float32)
face = torch.from_numpy(face).to(device)
# inference
with torch.no_grad():
cartoon = net(face)[0][0]
# post-process
cartoon = np.transpose(cartoon.cpu().numpy(), (1, 2, 0))
cartoon = (cartoon + 1) * 127.5
cartoon = (cartoon * mask + 255 * (1 - mask)).astype(np.uint8)
cartoon = cv2.cvtColor(cartoon, cv2.COLOR_RGB2BGR)
out_path = Path(tempfile.mkdtemp()) / "out.png"
cv2.imwrite(str(out_path), cartoon)
return out_path
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