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import os
import cv2
import torch
import numpy as np
from models import ResnetGenerator
import argparse
from utils import Preprocess
class Photo2Cartoon:
def __init__(self):
self.pre = Preprocess()
self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
self.net = ResnetGenerator(ngf=32, img_size=256, light=True).to(self.device)
curPath = os.path.abspath(os.path.dirname(__file__))
#assert os.path.exists('./models/photo2cartoon_weights.pt'), "[Step1: load weights] Can not find 'photo2cartoon_weights.pt' in folder 'models!!!'"
params = torch.load(os.path.join(curPath, 'models/photo2cartoon_weights.pt'), map_location=self.device)
self.net.load_state_dict(params['genA2B'])
print('[Step1: load weights] success!')
def inference(self, in_path):
img = cv2.cvtColor(cv2.imread(in_path), cv2.COLOR_BGR2RGB)
# face alignment and segmentation
face_rgba = self.pre.process(img)
if face_rgba is None:
print('[Step2: face detect] can not detect face!!!')
return None
print('[Step2: face detect] success!')
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(self.device)
# inference
with torch.no_grad():
cartoon = self.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)
print('[Step3: photo to cartoon] success!')
return cartoon
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